Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

2020

[1]

Baker L, Maley JH, Arévalo A, Francis DeMichele I, Mateo-Collado R, Finkelstein S, Celi LA. Real-world characterization of blood glucose control and insulin use in the intensive care unit. Sci Rep, 10:10718, July 2020. (doi:10.1038/s41598-020-67864-z) (PMID:32612144)

[2]

Danziger J, Armengol de la Hoz MÁ, Li W, Komorowski M, Deliberato RO, Rush BNM, Mukamal KJ, Celi L, Badawi O. Temporal trends in critical care outcomes in U.S. minority-serving hospitals. Am J Respir Crit Care Med, 201(6):681–687, Mar. 2020. (doi:10.1164/rccm.201903-0623OC) (PMID:31948262)

[3]

Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, Finkelstein S, Horng S, Celi LA. Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing. PLoS One, 15(3):e0229331, Mar. 2020. eCollection 2020. (doi:10.1371/journal.pone.0229331) (PMID:32126097)

[4]

Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, Finkelstein S, Horng S, Celi LA. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing. PLoS One, 15(4):e0230876, Apr. 2020. eCollection 2020. (doi:10.1371/journal.pone.0230876) (PMID:32240233)

[5]

Lai Y, Yeung W, Celi LA. Urban intelligence for pandemic response: Viewpoint. JMIR Public Health Surveill, 6(2):e18873, Apr. 2020. (doi:10.2196/18873) (PMID:32248145)

[6]

McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about artificial intelligence?. NPJ Digit Med, 3:86, June 2020. (doi:10.1038/s41746-020-0294-7) (PMID:32577533)

[7]

McLennan S, Celi LA, Buyx A. COVID-19: Putting the General Data Protection Regulation to the test. JMIR Public Health Surveill, 6(2):e19279, May 2020. (doi:10.2196/19279) (PMID:32449686)

[8]

Panch T, Pollard TJ, Mattie H, Lindemer E, Keane PA, Celi LA. ``Yes, but will it work for my patients?'' Driving clinically relevant research with benchmark datasets. NPJ Digit Med, 3:87, June 2020. eCollection 2020. (doi:10.1038/s41746-020-0295-6) (PMID:32577534)

[9]

Yeung W, Ng K, Fong JMN, Sng J, Tai BC, Chia SE. Assessment of proficiency of N95 mask donning among the general public in Singapore. JAMA Netw Open, 3(5):e209670, May 2020. (doi:10.1001/jamanetworkopen.2020.9670) (PMID:32432708)

2019

[1]

Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Impact of intensive care unit discharge delays on patient outcomes: A retrospective cohort study. J Intensive Care Med, 34(11–12):924–929, Nov. 2019. First published online: October 1, 2018. (doi:10.1177/0885066618800276) (PMID:30270722)

[2]

Bulgarelli L, Deliberato RO, Stone DJ, Celi LA, Johnson AEW. The authors reply. Crit Care Med, 47(7):e612, July 2019. (doi:10.1097/CCM.0000000000003786) (PMID:31205091)

[3]

Cosgriff CV, Celi LA, Ko S, Sundaresan T, de la Hoz MÁA, Kaufman AR, Stone DJ, Badawi O, Deliberato RO. Developing well-calibrated illness severity scores for decision support in the critically ill. NPJ Digit Med, 2:76, Aug. 2019. eCollection 2019. (doi:10.1038/s41746-019-0153-6) (PMID:31428687)

[4]

Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, Bonvini M, Celi LA, Douglas MJ. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. NPJ Digit Med, 2:116, Nov. 2019. eCollection 2019. (doi:10.1038/s41746-019-0192-z) (PMID:31815192)

[5]

Deliberato RO, Escudero GG, Bulgarelli L, Serpa Neto A, Ko SQ, Campos NS, Saat B, Amaro Júnior E, Lopes FS, Johnson AEW. SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries. Int J Med Inform, 131:103959, Nov. 2019. Epub 2019 Sep 4. (doi:10.1016/j.ijmedinf.2019.103959) (PMID:31539837)

[6]

Deliberato RO, Neto AS, Komorowski M, Stone DJ, Ko SQ, Bulgarelli L, Ponzoni CR, de Freitas Chaves RC, Celi LA, Johnson AEW. An evaluation of the influence of body mass index on severity scoring. Crit Care Med, 47(2):247–253, Feb. 2019. (doi:10.1097/CCM.0000000000003528) (PMID:30395555)

[7]

Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data, 6(1):317, Dec. 2019. (doi:10.1038/s41597-019-0322-0) (PMID:31831740)

[8]

Naik GS, Waikar SS, Johnson AEW, Buchbinder EI, Haq R, Hodi FS, Schoenfeld JD, Ott PA. Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition. J Immunother Cancer, 7(1):89, Mar. 2019. (doi:10.1186/s40425-019-0512-5) (PMID:30922394)

[9]

Reiz AN, García MS, Sagasti FM, González MÁ, Malpica AB, Benítez JCM, Cabrera MN, del Pino Ramírez Á, Perdomo JMG, Alonso JP, Celi LA, de la Hoz MÁA, Deliberato R, Paik K, Pollard T, Raffa J, Torres F, Mayol J, Chafer J, Ferrer AG, Rey A, Luengo HG. Big data and machine learning in critical care: Opportunities for collaborative research. Med Intensiva, 43(1):52–57, Jan–Feb 2019. [Article in English, Spanish] Epub 2018 Aug 2. (doi:10.1016/j.medin.2018.06.002) (PMID:30077427)

[10]

Sandfort V, Johnson AEW, Kunz LM, Vargas JD, Rosing DR. Prolonged elevated heart rate and 90-day survival in acutely ill patients: Data from the MIMIC-III database. J Intensive Care Med, 34(8):622–629, Aug. 2019. (doi:10.1177/0885066618756828) (PMID:29402151)

[11]

Serpa Neto A, Deliberato RO, Johnson AEW, Pollard TJ, Celi LA, Pelosi P, Gama de Abreu M, Schultz MJ, PROVE Network Investigators . Normalization of mechanical power to anthropometric indices: impact on its association with mortality in critically ill patients. Intensive Care Med, 45(12):1835–1837, Dec. 2019. Epub 2019 Oct 8. (doi:10.1007/s00134-019-05794-9) (PMID:31595350)

[12]

Vistisen ST, Johnson AEW, Scheeren TWL. Predicting vital sign deterioration with artificial intelligence or machine learning, Dec. 2019. Published: 28 June 2019. (doi:10.1007/s10877-019-00343-7) (PMID:31254239)

2018

[1]

Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Impact of Intensive Care Unit discharge delays on patient outcomes: A retrospective cohort study. J Intensive Care Med, 885066618800276, Oct. 2018. [Epub ahead of print]. (doi:10.1177/0885066618800276) (PMID:30270722)

[2]

Celi LA, Deliberato R, Vieira S. Foreword. Int J Med Inform, 113:96–97, May 2018. Epub 2018 Feb 23. (doi:10.1016/j.ijmedinf.2018.02.015) (PMID:29602439)

[3]

Deliberato RO, Ko S, Komorowski M, Armengol de La Hoz MÁ, Frushicheva MP, Raffa JD, Johnson AEW, Celi LA, Stone DJ. Severity of illness scores may misclassify critically ill obese patients. Crit Care Med, 46(3):394–400, Mar. 2018. (doi:10.1097/CCM.0000000000002868) (PMID:29194147)

[4]

Feng M, McSparron JI, Kien DT, Stone DJ, Roberts DH, Schwartzstein RM, Vieillard-Baron A, Celi LA. Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database. Intensive Care Med, 1–9, May 2018. [Epub ahead of print]. (doi:10.1007/s00134-018-5208-7) (PMID:29806057)

[5]

Johnson AEW, Aboab J, Raffa JD, Pollard TJ, Deliberato RO, Celi LA, Stone DJ. A comparative analysis of sepsis identification methods in an electronic database. Crit Care Med, Jan. 2018. [Epub ahead of print]. (doi:10.1097/CCM.0000000000002965) (PMID:29303796)

[6]

Lokhandwala S, McCague N, Chahin A, Escobar B, Feng M, Ghassemi MM, Stone DJ, Celi LA. One-year mortality after recovery from critical illness: A retrospective cohort study. PLoS ONE, 13(5):e0197226, May 2018. (doi:10.1371/journal.pone.0197226) (PMID:29750814)

[7]

Lyndon MP, P.Cassidy M, AnthonyCeli L, Hendrik L, Kim YJ, Gomez N, Baum N, Bulgarelli L, E.Paik K, Dagan A. Hacking Hackathons: Preparing the next generation for the multidisciplinary world of healthcare technology. Int J Med Inform, 112:1–5, Apr. 2018. Epub 2018 Jan 3. (doi:10.1016/j.ijmedinf.2017.12.020) (PMID:29500006)

[8]

Neto AS, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, Cazati DC, Cordioli RL, Correa TD, Pollard TJ, et al. Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med, 44(11):1914–1922, Nov. 2018. Epub 2018 Oct 5. (doi:10.1007/s00134-018-5375-6) (PMID:30291378)

[9]

Núñez Reiza A, de la Hoz MÁA, Sánchez García M. Big data analysis and machine learning in intensive care units. Med Intensiva, Dec. 2018. [Epub ahead of print] [Article in English, Spanish]. (doi:10.1016/j.medin.2018.10.007) (PMID:30591356)

[10]

Piza F, Celi LA, Deliberato RO, Bulgarelli L, de Carvalho FRT, Filho RR, Armengol de La Hoz MÁ, Kesselheim JC. Assessing team effectiveness and affective learning in a datathon. Int J Med Inform, 112:40–44, Apr. 2018. Epub 2018 Jan 11. (doi:10.1016/j.ijmedinf.2018.01.005) (PMID:29500020)

[11]

Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific Data, 5:180178, Sept. 2018. (doi:10.1038/sdata.2018.178) (PMID:30204154)

[12]

Pollard TJ, Johnson AEW, Raffa JD, Mark RG. tableone: An open source Python package for producing summary statistics for research papers. JAMIA Open, ooy012, May 2018. (doi:10.1093/jamiaopen/ooy012) (PMID:31984317)

[13]

Sauer CM, Sasson D, Paik KE, McCague N, Celi LA, Fernndez IS, Illigens BMW. Feature selection and prediction of treatment failure in tuberculosis. PLoS One, 13(11):e0207491, Nov. 2018. eCollection 2018. (doi:10.1371/journal.pone.0207491) (PMID:30458029)

[14]

Serpa Neto A, Kugener G, Bulgarelli L, Filho RR, Armengol de la Hoz MÁ, Johnson AEW, Paik KE, Torres F, Xie C, Jnior EA, Ferraz LJR, Celi LA, Deliberato RO. First Brazilian datathon in critical care. Rev Bras Ter Intensiva, 30(1):6–8, Jan–Mar 2018. (doi:10.5935/0103-507X.20180006) (PMID:29742215)

[15]

Severson KA, Ritter-Cox L, Raffa JD, Celi LA, Gordon WJ. Vasopressin administration is associated with rising serum lactate levels in patients with sepsis. J Intensive Care Med, 885066618794925, Aug. 2018. [Epub ahead of print]. (doi:10.1177/0885066618794925) (PMID:30130997)

[16]

Stretch R, Penna ND, Celi LA, Landon BE. Effect of boarding on mortality in ICUs. Crit Care Med, 46(4):525–531, Apr. 2018. (doi:10.1097/CCM.0000000000002905) (PMID:29252930)

[17]

Tyler PD, Du H, Feng M, Bai R, Xu Z, Horowitz GL, Stone DJ, Celi LA. Assessment of Intensive Care Unit laboratory values that differ from reference ranges and association with patient mortality and length of stay. JAMA Netw Open, 1(7):e184521, 2018. (doi:10.1001/jamanetworkopen.2018.4521) (PMID:30646358)

[18]

Vistisen ST, Moody B, Celi LA, Chen C. Post-extrasystolic characteristics in the arterial blood pressure waveform are associated with right ventricular dysfunction in intensive care patients. J Clin Monit Comput, Nov. 2018. [Epub ahead of print]. (doi:10.1007/s10877-018-0216-2) (PMID:30411186)

[19]

Zhu T, Johnson AEW, Yang Y, Clifford GD, Clifton DA. Bayesian fusion of physiological measurements using a signal quality extension. Physiological measurement, 39(6):065008, June 2018. (doi:10.1088/1361-6579/aac856) (PMID:29808824)

2017

[1]

Chen C, Lee J, Johnson AE, Mark RG, Celi LA, , Danziger J. Right ventricular function, peripheral edema, and acute kidney injury in critical illness. Kidney Int Rep, 2(6):1059–1065, June 2017. eCollection 2017 Nov. (doi:10.1016/j.ekir.2017.05.017) (PMID:29270515)

[2]

Clifford GD, Liu C, Moody B, Millet J, Schmidt S, Li Q, Silva I, Mark RG. Recent advances in heart sound analysis. Physiol Meas, 38(8):E10–E25, 2017. (doi:10.1088/1361-6579/aa7ec8) (PMID:28696334)

[3]

Deliberato RO, Celi LA, Stone DJ. Clinical note creation, binning, and artificial intelligence. JMIR Med Inform, 5(3):e24, 2017. (PDF) (doi:10.2196/medinform.7627) (PMID:28778845)

[4]

Deliberato RO, Rocha LL, Lima AH, Santiago CRM, Terra JCC, Dagan A, Celi LA. Physician satisfaction with a multi-platform digital scheduling system. PLoS ONE, 12(3):e0174127, 2017. (PDF) (doi:10.1371/journal.pone.0174127) (PMID:28328958)

[5]

Doocy S, Paik K, Lyles E, Tam HH, Fahed Z, Winkler E, Kontunen K, Mkanna A, Burnham G. Pilot testing and implementation of a mHealth tool for non-communicable diseases in a humanitarian setting. PLoS Curr,  9, June 2017. (doi:10.1371/currents.dis.e98c648aac93797b1996a37de099be74) (PMID:28744410)

[6]

Doocy S, Paik KE, Lyles E, Tam HH, Fahed Z, Winkler E, Kontunen K, Mkanna A, Burnham G. Guidelines and mHealth to improve quality of hypertension and type 2 diabetes care for vulnerable populations in Lebanon: Longitudinal cohort study. JMIR Mhealth Uhealth, 5(10):e158, Oct. 2017. (doi:10.2196/mhealth.7745) (PMID:29046266)

[7]

Fuchs L, Anstey M, Feng M, Toledano R, Kogan S, Howell MD, Clardy P, Celi L, Talmor D, Novack V. Quantifying the mortality impact of do-not-resuscitate orders in the ICU. Crit Care Med, 45(6):1019–1027, June 2017. (PDF) (doi:10.1097/CCM.0000000000002312) (PMID:28328651)

[8]

Fuchs L, Feng M, Novack V, Lee J, Taylor J, Scott D, Howell M, Celi L, Talmor D. The effect of ARDS on survival: do patients die from ARDS or with ARDS?. J Intensive Care Med, 885066617717659, July 2017. Epub ahead of print. (doi:10.1177/0885066617717659) (PMID:28681644)

[9]

Johnson AEW, Stone DJ, Celi LA, Pollard TJ. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc, Sept. 2017. (doi:10.1093/jamia/ocx084) (PMID:29036464)

[10]

Komorowski M, Celi LA. Will artificial intelligence contribute to overuse in healthcare?. Crit Care Med, 45(5):912–913, May 2017. (doi:10.1097/CCM.0000000000002351) (PMID:28410309)

[11]

Marshall DC, Salciccioli JD, Goodson RJ, Pimentel MA, Sun KY, Celi LA, Shalhoub J. The association between sodium fluctuations and mortality in surgical patients requiring intensive care. J Crit Care, 40:63–68, Aug. 2017. Epub 2017 Feb 13. (doi:10.1016/j.jcrc.2017.02.012) (PMID:28347943)

[12]

Moskowitz A, Chen KP, Cooper AZ, Chahin A, Ghassemi MM, Celi LA. Management of atrial fibrillation with rapid ventricular response in the intensive care unit: A secondary analysis of electronic health record data. Shock, 48(4):436–440, Oct. 2017. [Epub ahead of print]. (PMID:28328711)

[13]

Pathanasethpong A, Soomlek C, Morley K, Morley M, Polpinit P, Dagan A, Weis JW, Celi LA. Tackling regional public health issues using mobile health technology: Event report of an mHealth hackathon in Thailand. JMIR Mhealth Uhealth, 5(10):e155, Oct. 2017. (doi:10.2196/mhealth.8259) (PMID:29038098)

[14]

Pollard T, Celi LA. Enabling machine learning in critical care. ICU Management & Practice, 17(3), 2017. (PDF)

[15]

Rush B, Berger L, Celi LA. Access to palliative care for patients undergoing mechanical ventilation with idiopathic pulmonary fibrosis in the united states. Am J Hosp Palliat Care, 1049909117713990, June 2017. Epub ahead of print. (doi:10.1177/1049909117713990) (PMID:28602096)

[16]

Rush B, Martinka P, Kilb B, McDermid RC, Boyd JH, Celi LA. Acute respiratory distress syndrome in pregnant women. Obstet Gynecol, 129(3):530–535, 2017. (doi:10.1097/AOG.0000000000001907) (PMID:28178046)

[17]

Rush B, McDermid RC, Celi LA, Walley KR, Russell JA, H.Boyd J. Association between chronic exposure to air pollution and mortality in the acute respiratory distress syndrome. Environ Pollut, 224:352–356, May 2017. Epub 2017 Feb 13. (doi:10.1016/j.envpol.2017.02.014) (PMID:28202265)

[18]

Rush B, Walley KR, Celi LA, Rajoriya N, Brahmania M. Palliative care access for hospitalized patients with end-stage liver disease across the United States. Hepatology, June 2017. Epub ahead of print. (doi:10.1002/hep.29297) (PMID:28660622)

[19]

Rush B, Wiskar K, Celi LA, Walley KR, Russell JA, McDermid RC, Boyd JH. Association of household income level and in-hospital mortality in patients with sepsis: A nationwide retrospective cohort analysis. J Intensive Care Med, 885066617703338, Apr. 2017. Epub ahead of print. (doi:10.1177/0885066617703338) (PMID:28385107)

[20]

Tyler PD, Celi LA. Tele-ICU increases interhospital transfers: Does Big Brother know better?. Crit Care Med, 45(8):1417–1419, Aug. 2017. (doi:10.1097/CCM.0000000000002510) (PMID:28708685)

[21]

Wiens J, Snyder GM, Finlayson S, Mahoney MV, Celi LA. Potential adverse effects of broad-spectrum antimicrobial exposure in the Intensive Care Unit. Open Forum Infect Dis, 5(2):ofx270, Dec. 2017. eCollection 2018 Feb. (PDF) (doi:10.1093/ofid/ofx270) (PMID:29479546)

[22]

Wiskar K, Celi LA, Walley KR, Fruhstorfer C, Rush B. Inpatient palliative care referral and 9-month hospital readmission in patients with congestive heart failure: a linked nationwide analysis. J Intern Med, July 2017. Epub ahead of print. (doi:10.1111/joim.12657) (PMID:28741859)

[23]

Wiskar KJ, Celi LA, McDermid RC, Walley KR, Russell JA, Boyd JH, Rush B. Patterns of palliative care referral in patients admitted with heart failure requiring mechanical ventilation. Am J Hosp Palliat Care, 1049909117727455, Aug. 2017. Epub ahead of print. (doi:10.1177/1049909117727455) (PMID:28826226)

[24]

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, Doshi-Velez F. Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J Am Med Inform Assoc, 24(3):488–495, May 2017. (doi:10.1093/jamia/ocw138) (PMID:27707820)

2016

[1]

Aboab J, Celi LA, Charlton P, Feng M, Ghassemi M, Marshall DC, Mayaud L, Naumann T, McCague N, Paik KE, Pollard TJ, Resche-Rigon M, Salciccioli JD, Stone DJ. A datathon model to support cross-disciplinary collaboration. Science Translational Medicine, 8(333):333ps8, Apr. 2016. (PDF) (doi:10.1126/scitranslmed.aad9072) (PMID:27053770)

[2]

Angelidis P, Berman L, Casas-Perez ML, Celi LA, Dafoulas GE, Dagan A, Escobar B, Lopez DM, Noguez J, Osorio-Valencia JS, Otine C, Paik K, Rojas-Potosi L, Symeonidis AL, Winkler E. The hackathon model to spur innovation around global mHealth. J Med Eng Technol, 10:1–8, Aug. 2016. [Epub ahead of print]. (doi:10.1080/03091902.2016.1213903) (PMID:27538360)

[3]

Boone MD, Massa J, Mueller A, Jinadasa SP, Lee J, Kothari R, Scott DJ, Callahan J, Celi LA, Hacker MR. The organizational structure of an intensive care unit influences treatment of hypotension among critically ill patients: A retrospective cohort study. J Crit Care, pii: S0883–9441(16)00067–8, Feb. 2016. [Epub ahead of print]. (doi:10.1016/j.jcrc.2016.02.009) (PMID:26975737)

[4]

Celi LA, Lokhandwala S, Montgomery R, Moses C, Naumann T, Pollard T, Spitz D, Stretch R. Datathons and software to promote reproducible research. J Med Internet Res, 18(8):e230, Aug. 2016. (doi:10.2196/jmir.6365) (PMID:27558834)

[5]

Celi LA, Davidzon G, Johnson AEW, Komorowski M, Marshall DC, Nair SS, Phillips CT, Pollard TJ, Raffa JD, Salciccioli JD, Salgueiro FM, Stone DJ. Bridging the health data divide. J Med Internet Res, 18(12):e325, Dec. 2016. (PDF) (doi:10.2196/jmir.6400) (PMID:27998877)

[6]

Chen KP, Cavender S, Lee J, Feng M, Mark RG, Celi LA, Mukamal KJ, Danziger J. Peripheral edema, central venous pressure, and risk of AKI in critical illness. Clin J Am Soc Nephrol, 11(4):602–8, Apr. 2016. Epub 2016 Jan 19. (PDF) (doi:10.2215/CJN.08080715) (PMID:26787777)

[7]

Clifford GD, Silva I, Moody B, Li Q, Kella D, Chahin A, Kooistra T, Perry D, Mark RG. False alarm reduction in critical care. Physiol Meas, 37(8):E5–E23, Aug. 2016. Epub 2016 Jul 25. (PDF) (doi:10.1088/0967-3334/37/8/E5) (PMID:27454172)

[8]

Danziger J, Chen K, Cavender S, Lee J, Feng M, Mark RG, Mukamal KJ, Celi LA. Admission peripheral edema, central venous pressure, and survival in critically ill patients. Ann Am Thorac Soc, 13(5):705–11, May 2016. First published online 11 Mar 2016. (doi:10.1513/AnnalsATS.201511-737OC) (PMID:26966784)

[9]

Danziger J, Chen K, Lee J, Feng M, Mark RG, Celi L, Mukamal KJ. Obesity, acute kidney injury, and mortality in critical illness. Crit Care Med, 44(2):328–34, Feb. 2016. [Epub ahead of print]. (PDF) (doi:10.1097/CCM.0000000000001398) (PMID:26496453)

[10]

DePasse J, Celi LA. Collaboration, capacity building and co-creation as a new mantra in global health. Int J Qual Health Care, 28(4):536–7, Sept. 2016. Epub 2013 Nov 13. (doi:10.1093/intqhc/mzt077) (PMID:24225268)

[11]

Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proceedings of the IEEE, 104(2):444–466, Feb. 2016. (PDF) (doi:10.1109/JPROC.2015.2501978) (PMID:27765959)

[12]

Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data, 3:160035, May 2016. Published online 24 May 2016. (PDF) (doi:10.1038/sdata.2016.35) (PMID:27219127)

[13]

Katz DS, Niemeyer KE, Smith AM, Anderson WL, Boettiger C, Hinsen K, Hooft R, Hucka M, Lee A, Löffler F, Pollard T, Rios F. Software vs. data in the context of citation. PeerJ Preprints, 4:e2630v1, Dec. 2016. (PDF) (doi:10.7287/peerj.preprints.2630v1)

[14]

Lee J, Mark RG, Celi LA, Danziger J. Proton pump inhibitors are not associated with acute kidney injury in critical illness. J Clin Pharmacol, 56(12):1500–1506, Dec. 2016. [Epub ahead of print]. (doi:10.1002/jcph.805) (PMID:27492273)

[15]

Lehman LH, Mark RG, Nemati S. A model-based machine learning approach to probing autonomic regulation from nonstationary vital-signs time series. IEEE J Biomed Health Inform, PP(99):1, Dec. 2016. [Epub ahead of print]. (PDF) (doi:10.1109/JBHI.2016.2636808) (PMID:28114047)

[16]

Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AEW, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 37(12):2181–2213, Dec. 2016. Epub 2016 Nov 21. (PMID:27869105)

[17]

Lynch KE, Ghassemi F, Flythe JE, Feng M, Ghassemi M, Celi LA, Brunelli SM. Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit. Nephrology, 21(10):870–877, Oct. 2016. First published: 12 September 2016. (doi:10.1111/nep.12677) (PMID:26590371)

[18]

Moskowitz A, Lee J, Donnino MW, Mark R, Celi LA, Danziger J. The association between admission magnesium concentrations and lactic acidosis in critical illness. J Intensive Care Med, 31(3):187–92, Apr. 2016. [Epub 2014 Apr 14]. (PDF) (doi:10.1177/0885066614530659) (PMID:24733810)

[19]

Naidus E, Celi LA. Big data in healthcare: are we close to it?. Rev Bras Ter Intensiva, 28(1):8–10, Mar. 2016. (PDF) (PMID:27096670)

[20]

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, da Veiga Leprevost F, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaino JA. Ten simple rules for taking advantage of git and GitHub. PLoS Comput Biol, 12(7):e1004947, July 2016. eCollection 2016. (PDF) (doi:10.1371/journal.pcbi.1004947) (PMID:27415786)

[21]

Pimentel MAF, Brennan T, Lehman L, King NKK, Ang B, Feng M. Outcome prediction for patients with traumatic brain injury with dynamic features from intracranial pressure and arterial blood pressure signals: A gaussian process approach. Acta Neurochir Suppl, 122:85–91, 2016. (PDF) (doi:10.1007/978-3-319-22533-3_17) (PMID:27165883)

[22]

Rush B, Hertz P, Bond A, McDermid R, Celi LA. Utilization of palliative care in patients with end-stage chronic obstructive pulmonary disease on home oxygen: national trends and barriers to care in the United States. Chest, pii: S0012–3692(16)52413–1, July 2016. [Epub ahead of print]. (doi:10.1016/j.chest.2016.06.023) (PMID:27387892)

[23]

Rush B, Romano K, Ashkanani M, McDermid RC, Celi LA. Impact of hospital case-volume on subarachnoid hemorrhage outcomes: A nationwide analysis adjusting for hemorrhage severity. J Crit Care, pii: S0883–9441(16)30513–5, Sept. 2016. [Epub ahead of print]. (doi:10.1016/j.jcrc.2016.09.009) (PMID:27663296)

[24]

Shrime MG, Ferket BS, Scott DJ, Lee J, Barragan-Bradford D, Pollard T, Arabi YM, Al-Dorzi HM, Baron RM, Hunink MGM, Celi LA, Lai PS. Time-limited trials of intensive care for critically ill patients with cancer: How long is long enough?. JAMA Oncol., 2(1):76–83, Jan. 2016. [Published online October 15, 2015.]. (PDF) (doi:10.1001/jamaoncol.2015.3336) (PMID:26469222)

[25]

Stupple A, Geocadin RG, Celi LA. Conversation prior to resuscitation: The new CPR. Resuscitation, 99:e3, Feb. 2016. Published online 2015 Dec 29. (doi:10.1016/j.resuscitation.2015.12.006) (PMID:26740412)

[26]

Van Poucke S, Zhang Z, Schmitz M, Vukicevic M, Vander Laenen M, Celi LA, Deyne CD. Scalable predictive analysis in critically ill patients using a visual open data analysis platform. PLoS One, 11(1):e0145791, 2016. Published online 2016 Jan 5. (doi:10.1371/journal.pone.0145791) (PMID:26731286)

[27]

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, Doshi-Velez F. Understanding vasopressor intervention and weaning: Risk prediction in a public heterogeneous clinical time series database. J Am Med Inform Assoc, pii: ocw138, Oct. 2016. [Epub ahead of print]. (doi:10.1093/jamia/ocw138) (PMID:27707820)

2015

[1]

Badawi O, Brennan T, Celi LA, Feng M, Ghassemi M, Ippolito A, Johnson A, Mark RG, Mayaud L, Moody G, Moses C, Naumann T, Nikore V, Pimentel M, Pollard TJ, Santos M, Stone DJ, Zimolzak A. Metadata correction: making big data useful for health care: a summary of the inaugural MIT critical data conference. JMIR Med Inform, 3(1):e6, Jan. 2015. Correction to the article Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference in volume 2, e22. (doi:10.2196/medinform.4226) (PMID:25608565)

[2]

Byamba K, Syed-Abdul S, Garca-Romero MT, Huang CW, Nergyi S, Nyamdorj A, Nguyen PA, Iqbal U, Paik K, Celi LA, Nikore V, Somai M, Jian WS, Li YC. Mobile teledermatology for a prompter and more efficient dermatological care in rural Mongolia. Br J Dermatol, 173(1):265–7, July 2015. Epub 2015 May 12. (doi:10.1111/bjd.13607) (PMID:25494968)

[3]

Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting electronic health records systems: The next generation. JMIR Med Inform, 3(4):e34, Oct. 2015. (doi:10.2196/medinform.4192) (PMID:26500106)

[4]

Chen KP, Lee J, Mark RG, Feng M, Celi LA, Malley BE, Danziger J. Proton pump inhibitor use is not associated with cardiac arrhythmia in critically ill patients. J Clin Pharmacol, 55(7):774–9, July 2015. Epub 2015 Mar 16. (PDF) (doi:10.1002/jcph.479) (PMID:25655574)

[5]

de Louw EJ, Sun PO, Lee J, Feng M, Mark RG, Celi LA, Mukamal KJ, Danziger J. Increased incidence of diuretic use in critically ill obese patients. J Crit Care, 30(3):619–23, June 2015. Epub 2015 Feb 7. (PDF) (doi:10.1016/j.jcrc.2015.01.023) (PMID:25721030)

[6]

Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care, 19(1):118, Mar. 2015. (doi:10.1186/s13054-015-0801-4) (PMID:25886756)

[7]

Ghosh S, Feng M, Nguyen H, Li J. Hypotension risk prediction via sequential contrast patterns of icu blood pressure. IEEE J Biomed Health Inform, July 2015. [Epub ahead of print]. (PDF) (PMID:26168449)

[8]

Hsu DJ, Feng M, Kothari R, Zhou H, Chen KP, Celi LA. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: A propensity score analysis. Chest, 148(6):1470–1476, Aug. 2015. [Epub ahead of print]. (PDF) (doi:10.1378/chest.15-0516) (PMID:26270005)

[9]

Lee J, de Louw E, Niemi M, Nelson R, Mark RG, Celi LA, Mukamal KJ, Danziger J. Association between fluid balance and survival in critically ill patients. J Intern Med, 277(4):468–77, Apr. 2015. Epub 2014 Jun 27. (PDF) (doi:10.1111/joim.12274) (PMID:24931482)

[10]

Lehman LH, Adams RP, Mayaud L, Moody GB, Malhotra A, Mark RG, Nemati S. A physiological time series dynamics-based approach to patient monitoring and outcome prediction. IEEE J Biomed Health Inform, 19(3):1068–1076, May 2015. [Epub 2014 Jun 30]. (PDF) (doi:10.1109/JBHI.2014.2330827) (PMID:25014976)

[11]

Minhas MA, Velasquez AG, Kaul A, Salinas PD, Celi LA. Effect of protocolized sedation on clinical outcomes in mechanically ventilated intensive care unit patients: A systematic review and meta-analysis of randomized controlled trials. Mayo Clin Proc, 90(5):613–23, May 2015. Epub 2015 Apr 9. (doi:10.1016/j.mayocp.2015.02.016) (PMID:25865475)

[12]

Morgado E, Alonso-Atienza F, Santiago-Mozos R, Barquero-Pérez Ó, Silva I, Ramos J, Mark R. Quality estimation of the electrocardiogram using crosscorrelation among leads. BioMed Eng OnLine, 15:59, 2015. (PDF) (doi:10.1186/s1293801500531) (PMID:26091857)

[13]

Moskowitz A, McSparron J, Stone DJ, Celi LA. Preparing a new generation of clinicians for the era of big data. Harv Med Stud Rev, 2(1):24–27, Jan. 2015. (PMID:25688383)

[14]

Paonessa JR, Brennan T, Pimentel M, Steinhaus D, Feng M, Celi LA. Hyperdynamic left ventricular ejection fraction in the intensive care unit. Crit Care, 19:288, 2015. (PDF) (doi:10.1186/s13054-015-1012-8) (PMID:26250903)

[15]

Pereira RDMA, Salgado CM, Dejam A, Reti SR, Vieira SM, Sousa JMC, Celi LA, Finkelstein SN. Fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology. The Scientific World Journal, 2015. Article ID 212703, 9 pages. (PDF) (doi:10.1155/2015/212703) (PMID:26345130)

[16]

Salciccioli JD, Marshall DC, Pimentel MAF, Santos MD, Pollard T, Celi LA, Shalhoub J. The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study. Crit Care, 19:13, Jan. 2015. (doi:10.1186/s13054-014-0731-6) (PMID:25598149)

[17]

Shaw ND, Butler JP, Nemati S, Kangarloo T, Ghassemi M, Malhotra A, Hall JE. Accumulated deep sleep is a powerful predictor of lh pulse onset in pubertal children. J Clin Endocrinol Metab, 100(3):1062–1070, Mar. 2015. [First Published Online: December 09, 2014.]. (PDF) (doi:10.1210/jc.2014-3563) (PMID:25490277)

[18]

Silva I, Moody B, Behar J, Johnson A, Oster J, Clifford GD, Moody GB. Robust detection of heart beats in multimodal data. Physiol Meas, 36(8):1629–44, Aug. 2015. [Epub 2015 Jul 28]. (doi:10.1088/0967-3334/36/8/1629) (PMID:26217894)

[19]

Stone DJ, Celi LA, Csete M. Engineering control into medicine. J Crit Care, 30(3):652.e1–e7, June 2015. Epub 2015 Jan 30. (doi:10.1016/j.jcrc.2015.01.019) (PMID:25680579)

[20]

Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmic T, Celi LA. Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ, 93(3):203–8, Mar. 2015. Epub 2015 Jan 30. (doi:10.2471/BLT.14.139022) (PMID:25767300)

2014

[1]

Badawi O, Brennan T, Celi LA, Feng M, Ghassemi M, Ippolito A, Johnson A, Mark RG, Mayaud L, Moody G, Moses C, Naumann T, Pimentel M, Pollard TJ, Santos M, Stone DJ, Zimolzak A. Making big data useful for health care: A summary of the inaugural MIT critical data conference. JMIR Med Inform, 2(2):e22, 2014. (PDF) (doi:10.2196/medinform.3447) (PMID:25600172)

[2]

Boone MD, Celi LA, Ho BG, Pencina M, Curry MP, Lior Y, Talmor D, Novack V. Model for end-stage liver disease score predicts mortality in critically ill cirrhotic patients. J Crit Care, 29(5):881.e7–13, Oct. 2014. Epub 2014 May 28. (doi:10.1016/j.jcrc.2014.05.013) (PMID:24974049)

[3]

Celi LA, Csete M, Stone D. Optimal data systems: the future of clinical predictions and decision support. Curr Opin Crit Care, 20(5):573–80, Oct. 2014. (doi:10.1097/MCC.0000000000000137) (PMID:25137399)

[4]

Celi LA, Ippolito A, Montgomery RA, Moses C, Stone DJ. Crowdsourcing knowledge discovery and innovations in medicine. J Med Internet Res, 16(9):e216, June 2014. (PDF) (doi:10.2196/jmir.3761) (PMID:25239002)

[5]

Celi LA, Moseley E, Moses C, Ryan P, Somai M, Stone D, Tang K. From pharmacovigilance to clinical care optimization. Big Data, 2(3):134–141, Sept. 2014. Online ahead of print: August 13, 2014. (PDF) (doi:10.1089/big.2014.0008) (PMID:26576325)

[6]

Celi LA, Zimolzak AJ, Stone DJ. Dynamic clinical data mining: Search engine-based decision support. JMIR Med Inform, 2(1):e13, 2014. (PDF) (doi:10.2196/medinform.3110) (PMID:25600664)

[7]

Clifford GD, Silva I, Behar J, Moody GB. Non-invasive fetal ECG analysis. Physiol Meas, 35(8):1521–36, Aug. 2014. Epub 2014 Jul 29. (PDF) (doi:10.1088/0967-3334/35/8/1521) (PMID:25071093)

[8]

Dejam A, Malley BE, Feng M, Cismondi F, Park S, Samani S, Samani ZA, Pinto DS, Celi LA. The effect of age and clinical circumstances on the outcome of red blood cell transfusion in critically ill patients. Critical Care, 18(4):487, 2014. [Epub ahead of print]. (PDF) (doi:10.1186/s13054-014-0487-z) (PMID:25175389)

[9]

Fuchs L, Novack V, McLennan S, Celi LA, Baumfeld Y, Park S, Howell MD, Talmor DS. Trends in severity of illness on icu admission and mortality among the elderly. PLoS One, 9(4):e93234, Apr. 2014. (PDF) (doi:10.1371/journal.pone.0093234) (PMID:24699251)

[10]

Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest, 145(4):745–752, Apr. 2014. (doi:10.1378/chest.13-1722) (PMID:24371841)

[11]

Ghassemi MM, Richter SE, Eche IM, Chen TW, Danziger J, Celi LA. A data-driven approach to optimized medication dosing: a focus on heparin. Intensive Care Med, online publication, Aug. 2014. (PDF) (doi:10.1007/s00134-014-3406-5) (PMID:25091788)

[12]

Moseley ET, Hsu DJ, Stone DJ, Celi LA. Beyond open big data: Addressing unreliable research. J Med Internet Res, 16(11):e259, 2014. (PDF) (doi:10.2196/jmir.3871) (PMID:25405277)

[13]

Silva I, Moody G. An open-source toolbox for analysing and processing physionet databases in MATLAB and octave. Journal of Open Research Software, 2(1):e27, 2014. (PDF) (doi:10.5334/jors.bi) (PMID:26525081)

[14]

Velasquez A, Ghassemi M, Szolovits P, Park S, Osorio J, Dejam A, Celi L. Long-term outcomes of minor troponin elevations in the intensive care unit. Anaesth Intensive Care, 42(3):356–64, May 2014. (PDF) (PMID:24794476)

2013

[1]

Celi LA, Scott DJ, Lee J, Nelson R, Mukamal K, Mark R, Danziger J. Association of hypermagnesemia and blood pressure in the critically ill. J Hypertens, 31(11):2136–2141, Nov. 2013. discussion 2141. (PDF) (doi:10.1097/HJH.0b013e3283642f18) (PMID:24029865)

[2]

Celi LA, Mark RG, Stone DJ, Montgomery RA. Big Data in the Intensive Care Unit. Am J Respir Crit Care Med, 187(11):1157–1160, June 2013. (PDF) (doi:10.1164/rccm.201212-2311ED) (PMID:23725609)

[3]

Cismondi F, Celi LA, Fialho AS, Vieira SM, Reti SR, Sousa JMC, Finkelstein SN. Reducing unnecessary lab testing in the ICU with artificial intelligence. Int J Med Inform, 82(5):345–58, May 2013. [Epub 2012 Dec 28]. (PDF) (PMID:23273628)

[4]

Danziger J, William JH, Scott DJ, Lee J, Lehman L, Mark RG, Howell MD, Celi LA, Mukamal KJ. Proton-pump inhibitor use is associated with low serum magnesium concentrations. Kidney International, 83(4):692–699, Apr. 2013. Published online 16 January 2013. (PDF) (doi:10.1038/ki.2012.452) (PMID:23325090)

[5]

Fialho AS, Celi LA, Cismondi F, Vieira SM, Reti SR, Sousa JMC, Finkelstein SN. Disease-based modeling to predict fluid response in intensive care units. Methods Inf Med, 52(5), Aug. 2013. [Epub ahead of print]. (PDF) (PMID:23986268)

[6]

Fuchs L, Lee J, Novack V, Baumfeld Y, Scott D, Celi L, Mandelbaum T, Howell M, Talmor D. Severity of acute kidney injury and two-year outcomes in critically ill patients. Chest, 144(3):866–875, 2013. [Epub ahead of print]. (doi:10.1378/chest.12-2967) (PMID:23681257)

[7]

Lee J, Govindan S, Celi LA, Khabbaz KR, Subramaniam B. Customized prediction of short length of stay following elective cardiac surgery in elderly patients using a genetic algorithm. World J Cardiovasc Surg, 3(5):163–170, Sept. 2013. (PDF) (PMID:24482754)

[8]

Lehman LH, Saeed M, Talmor D, Mark R, Malhotra A. Methods of blood pressure measurement in the ICU. Crit Care Med, 41(1):34–40, Jan. 2013. (doi:10.1097/CCM.0b013e318265ea46) (PMID:23269127)

[9]

Mandelbaum T, Lee J, Scott DJ, Mark RG, Malhotra A, Howell MD, Talmor D. Empirical relationships among oliguria, creatinine, mortality, and renal replacement therapy in the critically ill. Intensive Care Med, 39(3):414–419, Dec. 2013. (T. Mandelbaum and J. Lee contributed equally to this work. Published online 7 December 2012.). (PDF) (doi:10.1007/s00134-012-2767-x) (PMID:23223822)

[10]

Mayaud L, Lai PS, Clifford GD, Tarrasenko L, Celi LA, Annane D. Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension. Crit Care Med, 41(4):954–962, Apr. 2013. (PMID:23385106)

[11]

Moses C, Celi LA, Marshall J. Pharmacovigilance: An active surveillance system to proactively identify risks for adverse events. Popul Health Manag, 16(3):147–9, June 2013. (doi:10.1089/pop.2012.0100) (PMID:23530466)

[12]

Perry W, Kwok A, Kozycki C, Celi LA. Disparities in end-of-life care: A perspective and review of quality. Popul Health Manag, 16(2):71–3, Apr. 2013. Epub 2013 Feb 13. (doi:10.1089/pop.2012.0061) (PMID:23405874)

[13]

Scott DJ, Lee J, Silva I, Park S, Moody GB, Celi LA, Mark RG. Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Med Inform Decis Mak, 13:9, Jan. 2013. (PDF) (doi:10.1186/1472-6947-13-9) (PMID:23302652)

2012

[1]

Celi LA, Galvin S, Davidzon G, Lee J, Scott D, Mark R. A database-driven decision support system: Customized mortality prediction. J Pers Med, 2(4):138–148, Sept. 2012. (doi:10.3390/jpm2040138) (PMID:23766893)

[2]

Celi LAG, Lee J, Scott DJ, Panch T, Mark RG. Collective experience: a database-fuelled, inter-disciplinary team-led learning system. J Comput Sci Eng, 6(1):51–59, 2012. (PDF) (doi:10.5626/JCSE.2012.6.1.51) (PMID:23766887)

[3]

Clifford GD, Moody GB. Signal quality in cardiorespiratory monitoring. Physiol Meas, 33(9), 2012. Focus issue: signal quality in cardiorespiratory monitoring. Gari D Clifford and George B Moody, Guest Editors. (PDF) (doi:10.1088/0967-3334/33/9/E01)

[4]

Fuchs L, Chronaki CE, Park S, Novack V, Baumfeld Y, Scott D, McLennan S, Talmor D, Celi L. ICU admission characteristics and mortality rates among elderly and very elderly patients. Intensive Care Med, 2012. Published online 15 July 2012. (PDF) (doi:10.1007/s00134-012-2629-6) (PMID:22797350)

[5]

Hunziker S, Celi LA, Lee J, Howell MD. Red cell distribution width improves the saps score for risk prediction in unselected critically ill patients. Crit Care, 16:R89, 2012. (PDF) (doi:10.1186/cc11351) (PMID:22607685)

[6]

Lee J, Kothari R, Ladapo JA, Scott DJ, Celi LA. Interrogating a clinical database to study treatment of hypotension in the critically ill. BMJ Open, 2012(2):e000916, 2012. (PDF) (doi:10.1136/bmjopen-2012-000916) (PMID:22685222)

[7]

Lee J, Nemati S, Silva I, Edwards BA, Butler JP, Malhotra A. Transfer entropy estimation and directional coupling change detection in biomedical time series. BioMed Eng OnLine, 11:19, 2012. (PDF) (doi:10.1186/1475-925X-11-19) (PMID:22500692)

[8]

Silva I, Lee J, Mark RG. Signal quality estimation with multi-channel adaptive filtering in intensive care settings. IEEE Trans Biomed Eng, 59(9):2476–85, Sept. 2012. Epub 2012 Jun 14. (PDF) (PMID:22717504)

2011

[1]

Celi LAG, Tang RJ, Villaroel M, Davidzon GA, Lester WT, Chueh HC. A clinical database-driven approach to decision support: Predicting mortality among patients with acute kidney injury. Journal of Healthcare Engineering, 2(1):97–110, Mar. 2011. (PMID:22844575)

[2]

Hug C, Clifford GD, Reisner AT. Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension. Crit Care Med, 39(5):1006–1014, May 2011. [Epub ahead of print]. (PDF) (doi:10.1097/CCM.0b013e31820eab8e) (PMID:21336136)

[3]

Mandelbaum T, Scott DJ, Lee J, Mark RG, Malhotra A, Waikar S, Howell MD, Talmor DS. Outcome of critically ill patients with acute kidney injury using the Acute Kidney Injury Network criteria. Crit Care Med, 39(12):2659–2664, Dec. 2011. Preprint available online 14 July 2011. (PMID:21765352)

[4]

Nemati S, Abdala O, Bazan V, Tim-Yeh S, Malhotra A, Clifford GD. A non-parametric surrogate-based test of significance for T-wave alternans detection. IEEE Transactions On Biomedical Engineering, 58(5):1356–64, May 2011. Epub Apr 19, 2010. (PDF) (doi:10.1109/TBME.2010.2047859) (PMID:20409986)

[5]

Nemati S, Malhotra A, Clifford GD. T-wave alternans patterns during sleep in healthy, cardiac disease, and sleep apnea patients. J Electrocardiol, 44(2):126–30, Mar–Apr 2011. Epub Dec 15, 2010. (PDF) (doi:10.1016/j.jelectrocard.2010.10.036) (PMID:21163493)

[6]

Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med, 39(5):952–960, 2011. (PDF) (doi:10.1097/CCM.0b013e31820a92c6) (PMID:21283005)

2010

[1]

Campana LM, Owens RL, Clifford GD, Pittman SD, Malhotra A. Phase rectified signal averaging as a sensitive index of autonomic changes with aging. J Appl Physiol, 108(6):1668–1673, June 2010. E-print ahead of publication: March 25, 2010. (PDF) (doi:10.1152/japplphysiol.00013.2010) (PMID:20339014)

[2]

Clifford GD, Nemati S, Sameni R. An artificial vector model for generating abnormal electrocardiographic rhythms. Physiol Meas, 31(5):595–609, May 2010. IOP 'Featured Article'. (PDF) (doi:10.1088/0967-3334/31/5/001) (PMID:20308774)

[3]

Heldt T, Mukkamala R, Moody GB, Mark RG. CVSim: an open-source cardiovascular simulator for teaching and research. The Open Pacing, Electrophysiology, and Therapy Journal, 3:45–54, 2010. (PDF) (doi:10.2174/1876536X01003010045) (PMID:21949555)

[4]

Lee J, Mark RG. An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomed Eng Online, 9:62, Oct. 2010. (PDF) (doi:10.1186/1475-925X-9-62) (PMID:20973998)

[5]

Monasterio V, Clifford GD, Laguna P, Martínez JP. A multilead scheme based on periodic component analysis for T wave alternans analysis in the ECG. Ann Biomed Eng, 38(8):2532–2541, Aug. 2010. (PDF) (doi:10.1007/s10439-010-0029-z) (PMID:20387121)

[6]

Nemati S, Malhotra A, Clifford GD. Data fusion for improved respiration rate estimation. EURASIP Journal on Advances in Signal Processing, 2010(926305):1–10, May 2010. (PDF) (doi:10.1155/2010/926305) (PMID:20806056)

[7]

Sayadi O, Shamsollahi MB, Clifford GD. Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Transactions on Biomedical Engineering, 57(2):353–362, Feb. 2010. (PDF) (doi:10.1109/TBME.2009.2031243) (PMID:19758851)

[8]

Sayadi O, Shamsollahi MB, Clifford GD. Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model. Physiol Meas, 31(10):1309–29, Oct. 2010. Epub Aug 18, 2010. (PDF) (PMID:20720288)

[9]

Silva I, Epstein M. Estimating loudness growth from tone-burst evoked responses. J Acoust Soc Am, 127(6):3629–3642, 2010. (PDF) (doi:10.1121/1.3397457) (PMID:20550262)

2009

[1]

Celi LA, Sarmenta L, Rotberg J, Marcelo A, Clifford GD. Mobile care (Moca) for remote diagnosis and screening. Journal of Health Informatics in Developing Countries, 3(1):17–21, 2009. (PDF) (PMID:21822397)

[2]

Clifford GD, Long WJ, Moody GB, Szolovits P. Robust parameter extraction for decision support using multimodal intensive care data. Phil Trans Royal Soc A, 367(1877):411–429, Jan. 2009. Special issue on Signal Processing in Vital Rhythms and Signs. (PDF) (doi:10.1098/rsta.2008.0157) (PMID:18936019)

[3]

Li Q, Mark RG, Clifford GD. Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator. Biomed Eng Online, 8(13), July 2009. (doi:10.1186/1475-925X-8-13) (PMID:19586547)

[4]

Moody GB. Physionet: Research resource for complex physiologic signals. [Japanese Journal of Electrocardiology], 29:1–3, 2009. (PDF)

[5]

Sun JX, Reisner AT, Saeed M, Heldt T, Mark RG. The cardiac output from blood pressure algorithms trial. Crit Care Med, 37(1):72–80, Jan. 2009. (PDF) (PMID:19112280)

2008

[1]

Aboukhalil A, Nielsen L, Saeed M, Mark RG, Clifford GD. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. J Biomed Inform, 41(3):442–451, June 2008. (doi:10.1016/j.jbi.2008.03.003) (PMID:18440873)

[2]

Clifford GD, Blaya JA, Hall-Clifford R, Fraser HSF. Medical information systems: A foundation for healthcare technologies in developing countries. BMC Biomed Eng Online, 7(1):18, 2008. (doi:10.1186/1475-925X-7-18) (PMID:18547411)

[3]

Dawoud F, Wagner G, Moody G, Horácek B. Using inverse electrocardiography to image myocardial infarction–reflecting on the 2007 PhysioNet/Computers in Cardiology Challenge. J Electrocardiol, 41(6):630–5, 2008. (PMID:18954610)

[4]

Jia X, Malhotra A, Saeed M, Mark RG, Talmor D. Risk factors for Acute Respiratory Distress Syndrome in patients mechanically ventilated for > 48 h. Chest, 133(4):853–861, Apr. 2008. (doi:10.1378/chest.07-1121) (PMID:18263691)

[5]

Li Q, Mark RG, Clifford GD. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. IOP Physiol Meas, 29(1):15–32, Jan. 2008. (Awarded the Martin Black Prize for Best Paper in Physiological Measurement in 2008). (PDF) (PMID:18175857)

[6]

Neamatullah I, Douglass M, Lehman LH, Reisner A, Villarroel M, Long WJ, Szolovits P, Moody GB, Mark RG, Clifford GD. Automated de-identification of free-text medical records. BMC Med Inform Decis Mak, 8:32, July 2008. (PDF) (doi:10.1186/1472-6947-8-32) (PMID:18652655)

[7]

Wolfberg AJ, DeRosier DJ, Roberts T, Syed Z, Clifford GD, Acker D, du Plessis AJ. A comparison of subjective and mathematical estimations of fetal heart rate variability. Journal of Maternal-Fetal and Neonatal Medicine, 21(2):101–4, 2008. (doi:10.1080/14767050701836792) (PMID:18240077)

2007

[1]

Lian J, Clifford GD, Müessig D, Lang V. Open source model for generating RR intervals in atrial fibrillation and beyond. BioMedical Engineering OnLine, 6(9):1–16, Mar. 2007. doi:10.1186/1475-925X-6-9. (PDF) (PMID:17335580)

[2]

Sameni R, Clifford GD, Shamsollahi MB, Jutten C. Multi-channel ECG and noise modeling: application to maternal and fetal ECG signals. EURASIP Journal on Advances in Signal Processing, 2007(43407):1–14, 2007. (PDF) (doi:10.1155/2007/43407)

[3]

Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng, 54(12):2172–2185, Dec. 2007. (PDF) (PMID:18075033)

2006

[1]

Clifford GD. A novel framework for signal representation and source separation. Journal of Biological Systems, 14(2):169–183, June 2006. (PDF)

[2]

He T, Clifford GD, Tarassenko L. Application of ICA in removing artefacts from the ECG. Neural Comput and Applic, 15(2):105–116, 2006. (PDF)

[3]

Heldt T. Continuous blood pressure-derived cardiac output monitoring — should we be thinking long term? J Appl Physiol, 101(2):373–374, 2006. Invited editorial. (PDF) (PMID:16690788)

[4]

Mukkamala R, Reisner TA, Hojman H, Mark RG, Cohen RJ. Continuous cardiac output monitoring by peripheral blood pressure waveform analysis. IEEE Trans Biomed Eng, 53(3):459–467, 2006. (PDF) (doi:10.1109/TBME.2005.869780) (PMID:16532772)

[5]

Parlikar TA, Heldt T, Verghese GC. Cycle-averaged models of cardiovascular dynamics. IEEE Transactions on Circuits and Systems–I: Fundamental Theory and Applications, 53(11):2459–2468, 2006. (PDF)

2005

[1]

Clifford GD, Tarassenko L. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Transactions in Biomedical Engineering, 52(4):630–638, 2005. (PDF) (doi:10.1109/TBME.2005.844028) (PMID:15825865)

[2]

Clifford GD, Shoeb A, McSharry PE, Janz BA. Model-based filtering, compression and classification of the ECG. International Journal of Bioelectromagnetism, 7(1):158–161, 2005. (PDF)

[3]

Heldt T, Chang JL, Chen JJS, Verghese GC, Mark RG. Cycle-averaged dynamics of a periodically driven, closed loop circulation model. Control Eng Pract, 13(9):1163–1171, Sept. 2005. (PDF) (PMID:16050064)

2004

[1]

Clifford GD, Tarassenko L. Segmenting cardiac-related data using sleep stages increases separation between normal subjects and apnoeic patients. IOP Physiol Meas, (25):N27–N35, 2004. (PDF) (doi:10.1088/0967-3334/25/6/N03) (PMID:15712732)

[2]

Jager F, Moody GB, Mark RG. Protocol to assess robustness of ST analysers: a case study. Physiological Measurement, 25(3):629–643, 2004. (PDF) (doi:10.1088/0967-3334/25/3/004) (PMID:15253115)

[3]

Zong W, Moody GB, Mark RG. Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure. Med Biol Eng Comput, 42(5):698–706, Sept. 2004. (PDF) (PMID:15503972)

2003

[1]

Costa M, Moody GB, Henry I, Goldberger AL. Physionet: an NIH research resource for complex signals. J Electrocardiology, 36(suppl):139–144, 2003. (PDF)

[2]

Jager F, Taddei A, Moody GB, Emdin M, Antolic G, Dorn R, Smrdel A, Marchesi C, Mark RG. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med Biol Eng Comput, 41(2):172–182, Mar. 2003. (PMID:12691437)

[3]

McSharry PE, Clifford GD, Tarassenko L. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng, 50(3):289–294, 2003. (PDF) (doi:10.1109/TBME.2003.808805) (PMID:12669985)

Conference proceedings and presentations

2020

[1]

Bose S, Lehman L, Huang K, Talmor D, Shahn Z. Should diuretic initiation be delayed in ICU patients with recent vasopressor use? A causal analysis. Crit Care Med, 48(1):733, Jan. 2020. Research Snapshot Theater: Resuscitation V. Presented at the 49th Critical Care Congress in Orlando, FL (https://www.sccm.org/Education-Center/Annual-Congress/). (doi:10.1097/01.ccm.0000647968.73423.76)

2019

[1]

Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, Bonvini M, Celi LA, Douglas MJ. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients, bringing context to abnormal admission lab values. Presented at the European Society of Intensive Care Medicine 32nd Annual Congress, Berlin (https://www.esicm.org/events/32nd-annual-congress-berlin/), Sept. 2019.

[2]

Lanius SM, Chen CW, Johnson AEW, Celi LA, Law AC. Outcome in dialyzed ICU patients in septic shock. In Am J Kidney Dis, volume 73, 693, May 2019. (doi:https://doi-org.ezproxy.canberra.edu.au/10.1053/j.ajkd.2019.03.199)

[3]

Raffa J, Johnson A, Celi LA, Pollard T, Pilcher D, Badawi O. The global open source severity of illness score (GOSSIS). Crit Care Med, 47(1):17, 2019. (doi:10.1097/01.ccm.0000550825.30295.dd)

[4]

Rincon T, Celi LA, Raffa J, Koestler D, Pollard T, Johnson A, Pierce J. Prognostic accuracy of the sofa score and a sepsis prompt in discriminating sepsis. Critical Care Medicine, 47(1):759, Jan. 2019. (doi:10.1097/01.ccm.0000552309.57308.ff)

2018

[1]

Angelotti G, Morandini P, Lehman L, Mark R, Barbieri R. The role of baroreflex sensitivity in acute hypotensive episodes prediction in the Intensive Care Unit. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2784–2787, July 2018. (PDF) (doi:10.1109/EMBC.2018.8512859)

[2]

Elahinia H, Raffa JD, Wu JT, Ghassemi MM. Predicting medical nonadherence using natural language processing. In 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Feb. 2018. (doi:10.1109/URTC.2017.8284212)

[3]

Ghassemi MM, Al-Hanai T, Raffa JD, Mark RG, Nemati S, Chokshi FH. How is the doctor feeling? ICU provider sentiment is associated with diagnostic imaging utilization. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4058–4064, July 2018. (PDF) (doi:10.1109/EMBC.2018.8513325)

[4]

Ghassemi MM, Moody BE, Lehman LH, Song C, Li Q, Sun H, Mark RG, Westover MB, Clifford GD. You snooze, you win: the PhysioNet/Computing in Cardiology Challenge 2018. Comput Cardiol, 45, Sept. 2018. (PDF) (doi:10.22489/CinC.2018.049)

[5]

Johnson AEW, Pollard TJ, Naumann T. Generalizability of predictive models for intensive care unit patients. In ML4H: Machine Learning for Health, Dec. 2018. Workshop at NeurIPS 2018.

[6]

Lehman EP, Krishnan RG, Zhao X, Mark RG, Lehman LwH. Representation learning approaches to detect false arrhythmia alarms from ECG dynamics. In Doshi-Velez F, Fackler J, Jung K, Kale D, Ranganath R, Wallace B, Wiens J, editors, Proceedings of the 3rd Machine Learning for Healthcare Conference, volume 85, 571–586, Palo Alto, California, 17–18 Aug 2018. PMLR. (PDF)

[7]

Ren O, Johnson AEW, Lehman EP, Komorowski M, Aboab J, Tang F, Shahn Z, Sow D, Mark R, Lehman L. Predicting and understanding unexpected respiratory decompensation in critical care using sparse and heterogeneous clinical data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), 144–151, June 2018. (PDF) (doi:10.1109/ICHI.2018.00024)

2017

[1]

Clifford G, Liu C, Moody B, Silva I, Li Q, Johnson A, Mark R. AF classification from a short single lead ECG recording: the PhysioNet Computing in Cardiology Challenge 2017. Comput Cardiol, 44, 2017. In press.

[2]

Dai Y, Lokhandwala S, Long W, Mark R, Lehman LH. Phenotyping hypotensive patients in critical care using hospital discharge summaries. Proc IEEE Intl Conf Biomed Health Inform, 2017. (PDF)

[3]

Ghassemi MM, Jarvis W, Alhanai T, Brown EN, Mark RG, Westover MB. An open-source tool for the transcription of paper-spreadsheet data. In 2017 IEEE International Conference on Big Data (Big Data), 935–941, Dec. 2017. (PDF)

[4]

Johnson AEW, Mark RG. Real-time mortality prediction in the Intensive Care Unit. In AMIA Annu Symp Proc 2017, 994–1003, Nov. 2017. eCollection 2017. (PDF)

[5]

Johnson AEW, Pollard TJ, Mark RG. Reproducibility in critical care: a mortality prediction case study. In Doshi-Velez F, Fackler J, Kale D, Ranganath R, Wallace B, Wiens J, editors, Proceedings of the 2nd Machine Learning for Healthcare Conference, volume 68, 361–376, Boston, Massachusetts, 18–19 Aug 2017. PMLR. (PDF)

[6]

Lehman L, Johnson A, Sudduth C, Mark R, Nemati S. Dynamics of multivariate vital sign time series and severe sepsis among patients in critical care. J Crit Care, 38:365, Apr. 2017. (doi:10.1016/j.jcrc.2016.11.021)

[7]

Zalewski A, Long W, Johnson AEW, Mark RG, Lehman LH. Estimating patient’s health state using latent structure inferred from clinical time series and text. Proc IEEE Intl Conf Biomed Health Inform, 2017. (PDF)

2016

[1]

Adibuzzaman M, Musselman K, Johnson A, Brown P, Pitluk Z, Grama A. Closing the data loop: An integrated open access analysis platform for the MIMIC database. Comput Cardiol, 43:205–208, 2016. (PDF) (doi:10.22489/CinC.2016.043-205)

[2]

Bonvini M, Kaufman A, Ramazzotti D, Celi LA, Stretch R. Comparison of imputation methods to predict baseline serum creatinine. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000509965.53628.fb)

[3]

Bose S, Moskowitz A, Jalilian L, Celi LA, Johnson AEW. Impact of intensive care unit discharge delays. Am J Respir Crit Care Med, 193:A4695–A4695, 2016. Presentation at the American Thoracic Society 2016 International Conference, May 13–18, 2016, San Francisco.

[4]

Chen C, Celi LA. Left ventricular diastolic dysfunction and hospital mortality. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508844.84122.82)

[5]

Clifford GD, Liu C, Moody B, Springer D, Silva I, Li Q, Mark RG. Classification of normal/abnormal heart sound recordings: the PhysioNet/Computing in Cardiology Challenge 2016. Comput Cardiol, 43:609–612, 2016. (PDF)

[6]

Della Penna N, Stretch R, Celi LA. Mortality heterogeneity of geographic co-localization of intensive care unit patient and care team. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000509819.41485.8f)

[7]

Johnson A, Celi LA, Raffa J, Pollard T, Ston D. External validation of the sepsis-3 guidelines. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508736.93826.b5)

[8]

Lehman LH, Johnson A, Sudduth C, Mark R, Nemati S. Dynamics of multivariate vital sign time series and severe sepsis among patients in critical care. Presented at the 15th International Conference on Complex Acute Illness Conference (ICCAI), Pasadena, California, USA, Aug. 2016.

[9]

Marshall JD, You CX, Pollard T, Salgueiro F, Chen C, Celi LA. Impact of left ventricular heart failure with preserved ejection fraction and right ventricular systolic heart failure on outcomes in the intensive care unit. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[10]

Pacheco R, Salgado C, Deliberato R, Celi LA, Sousa J, Vieira S. Modeling to individualize mean arterial pressure threshold to prevent acute kidney injury in the ICU. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508810.24919.2c)

[11]

Pollard T. Crowdsourcing research communities to solve problems in critical care. Oral presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[12]

Pollard TJ. An introduction to the MIMIC-III Critical Care Database. Presented at the London Critical Care Datathon (http://datascicc.org/), Dec. 2016. (PDF)

[13]

Pollard T, Komorowski M, Salciccioli JD, Marshall DC, Sykes M, Goodson R, Hartley A, Shalhoub J. Lactate rebound as an independent predictor of mortality in the intensive care unit. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016. (PDF)

[14]

Raffa JD, Montgomery RA, Stretch R, Johnson AEW, Celi LA, Pollard T. Trends in mechanical ventilation and vasopressor use and relevance to mortality outcomes in critical care settings. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[15]

Sun F, Cui A, Lokhandwala S, Tyler P, Shen M, Paul D, Pollard T. Risk factors for mortality in critically ill patients requiring new renal replacement therapy. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[16]

Tyler P, Celi LA, Rush B. Interhospital transfer of patients with sepsis across the United States. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000510016.10287.46)

2015

[1]

Chronaki C, Shahin A, Mark R. Designing reliable cohorts of cardiac patients across MIMIC and eICU. Comput Cardiol, 42:189–192, 2015. (PDF)

[2]

Clifford G, Silva I, Moody B, Li Q, Kella D, Shahin A, Kooistra T, Perry D, Mark R. The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU. Comput Cardiol, 42:273–276, 2015. (PDF)

[3]

Ghassemi MM, Amorim E, Pati SB, Mark RG, Brown EN, Purdon PL, Westover MB. An enhanced cerebral recovery index for coma prognostication following cardiac arrest. Conf Proc IEEE Eng Med Biol Soc, 2015:534–7, 2015. (PDF) (PMID:26736317)

[4]

Ghassemi M, Pimentel MA, Naumann T, Brennan T, Clifton DA, Szolovits P, Fengr M. A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data. Proc Conf AAAI Artif Intell, 2015:446–453, Jan. 2015. (PMID:27182460)

[5]

Ghassemi MM, Mark RG, Nemati S. A visualization of evolving clinical sentiment using vector representations of clinical notes. Comput Cardiol, 42:629–632, 2015. (PDF) (PMID:27774487)

[6]

Lehman LH, Ghassemi M, Snoek J, Nemati S. Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach. Comput Cardiol, 42:1069–1072, 2015. (PDF)

[7]

Lehman LH, Nemati S, Mark RG. Hemodynamic monitoring using switching autoregressive dynamics of multivariate vital sign time series. Comput Cardiol, 42:1065–1068, 2015. (PDF)

[8]

Pollard T, Komorowski M, Johnson A, Salciccioli J. Critical care datathon: Answering clinically relevant questions with the mimic critical care datase. Presentation at the Mozilla Festival, 2015, Nov. 2015.

2014

[1]

Ghassemi M, Lehman LH, Snoek J, Nemati S. Global optimization approaches for parameter tuning in biomedical signal processing: A focus of multi-scale entropy. Comput Cardiol, 41:993–996, 2014. (PDF)

[2]

Ghosh S, Feng M, Nguyen H, Li J. Predicting heart beats using co-occurring constrained sequential patterns. Comput Cardiol, 41:265–268, 2014. (PDF)

[3]

Lehman LH, Long W, Saeed M, Mark RG. Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort. Proceedings of the 36th International Conference of the IEEE Engineering in Medicine and Biology Society, 1773–1776, Aug. 2014. (PDF) (doi:10.1109/EMBC.2014.6943952)

[4]

Lehman LH, Nemati S, Moody G, Heldt T, Mark RG. Uncovering clinical significance of vital sign dynamics in critical care. Comput Cardiol, 41:1141–1144, 2014. (PDF)

[5]

Moody GB, Moody B, Silva I. Robust detection of heart beats in multimodal data: the PhysioNet/Computing in Cardiology Challenge 2014. Comput Cardiol, 41:549–552, 2014. (PDF)

[6]

Naumann T, Silva I. Scaling the WFDB Toolbox for MATLAB and Octave. Comput Cardiol, 41:161–164, 2014. (PDF)

[7]

Springer DB, Brennan T, Hitzeroth J, Mayosi BM, Tarassenko L, Clifford GD. Robust heart rate estimation from noisy phonocardiograms. Comput Cardiol, 41:613–616, 2014. (PDF)

[8]

Zhang Z, Ghassemi M, Silva I, Ainslie P, Celi LA, Cheng GZ. Modeling circadian rhythm variations during sepsis. Am J Respir Crit Care Med, B105. SEPSIS: CARE MODELS AND OUTCOMES:A3795, May 2014.

2013

[1]

Lehman LH, Nemati S, Adams RP, Moody G, Malhotra A, Mark RG. Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series. Conf Proc IEEE Eng Med Biol Soc, 7072–5, 2013. (PDF) (doi:10.1109/EMBC.2013.6611187) (PMID:24111374)

[2]

Moody GB. LightWAVE: Waveform and annotation viewing and editing in a web browser. Comput Cardiol, 40:17–20, 2013. (PDF)

[3]

Nemati S, Lehman LH, Adams RP. Learning outcome-discriminative dynamics in multivariate physiological cohort time series. Conf Proc IEEE Eng Med Biol Soc, 7104–7, 2013. (PDF) (doi:10.1109/EMBC.2013.6611195) (PMID:24111382)

[4]

Silva I, Behar J, Sameni R, Zhu T, Oster J, Clifford GD, Moody GB. Noninvasive fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013. Comput Cardiol, 40:149–152, 2013. (PDF) (PMID:25401167)

2012

[1]

Berg KM, Ghassemi M, Donnino MW, Marshall J, Celi L. Pre-admission use of selective serotonin reuptake inhibitors is associated with icu mortality. Poster presentation [Poster Board #224] at the American Thoracic Society International Conference, San Francisco, May 18–23, 2012.

[2]

Lehman L, Nemati S, Adams RP, Mark R. Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU. Conf Proc IEEE Eng Med Biol Soc. 2012, 5939–42, 2012. (PDF) (PMID:23367281)

[3]

Lehman L, Saeed M, Long W, Lee J, Mark R. Risk stratification of ICU patients using topic models inferred from unstructured progress notes. AMIA Annu Symp Proc, 505–11, Nov. 2012. (PDF) (PMID:23304322)

[4]

Nemati S, Lehman L, Adams RP, Malhotra A. Discovering shared cardiovascular dynamics within a patient cohort. Proc 34th IEEE EMBS, 2012. (PDF)

[5]

Silva I, Moody GB, Scott DJ, Celi LA, Mark RG. Predicting in-hospital mortality of ICU patients: the PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol, 39:245–248, 2012. (PDF) (PMID:24678516)

[6]

Silva I, Moody G, Scott DJ, Celi LA, Mark RG. Predicting in-hospital mortality of ICU patients: The PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol, 39:245–248, 2012. (PDF)

2011

[1]

Lee J, Scott DJ, Villarroel M, Clifford GD, Saeed M, Mark RG. Open-access MIMIC-II database for intensive care research. Conf Proc IEEE Eng Med Biol Soc. 2011, 8315–8318, 2011. (PDF) (PMID:22256274)

[2]

Mandelbaum T, Scott DJ, Lee J, Mark RG, Howell MD, Malhotra A, Talmor D. Validation of the AKIN criteria definition using high-resolution ICU data from the MIMIC-II database. Critical Care, 15(Suppl 1):105, 2011. (doi:10.1186/cc9525)

[3]

Moody BE. A rule-based method for ECG quality control. Comput Cardiol, 38:361–363, 2011. (PDF)

[4]

Moody GB, Mark RG, Goldberger AL. PhysioNet: physiologic signals, time series, and related open source software for basic, clinical, and applied research. Proc 33rd IEEE EMBS, 8327–8330, 2011. (PDF) (PMID:22256277)

[5]

Silva I, Lee J, Mark RG. Photoplethysmograph quality estimation through multichannel filtering. Conf Proc IEEE Eng Med Biol Soc. 2011, 4361–4364, 2011. (PDF) (PMID:22255305)

[6]

Silva I, Moody G, Celi L. Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011. Comput Cardiol, 38:273–276, 2011. (PDF)

2010

[1]

Celi LA, Hug C, Villarroel M, Clifford G, Mark R. Issues with data mining: predictive modeling on critically ill patients who develop acute renal failure. Crit Care Med, Jan. 2010.

[2]

Celi LA, Villarroel M, Davidzon G, Galvin S, Clifford G, Galvin I, Bunton R, Szolovits P. Comparing the performance of customized mortality prediction models using local database against current standard scoring systems. Crit Care Med, Jan. 2010.

[3]

Craig M, Moody B, Jia S, Villarroel M, Mark R. Matching data fragments with imperfect identifiers from disparate sources. Comput Cardiol, 37:793–796, Sept. 2010. (PDF)

[4]

Kashif FM, Heldt T, Novak V, Czosnyka M, Verghese GV. Model-based cerebrovascular monitoring. Oral contribution, American Heart Association 2010 International Stroke Conference, Feb. 2010.

[5]

Lee J, Mark RG. A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series. Comput Cardiol, 37:81–84, Sept. 2010. (PDF)

[6]

Lehman L, Saeed M, Moody GB, Mark RG. Hypotension as a risk factor for acute kidney injury in ICU patients. Comput Cardiol, 37:1095–1098, Sept. 2010. (PDF)

[7]

Lojun SL, Sauper CJ, Medow M, Long WJ, Mark RG, Barzilay R. Investigating resuscitation code assignment in the intensive care unit using structured and unstructured data. AMIA Annu Symp Proc, 2010:467–471, 2010. (PDF) (PMID:21347022)

[8]

Mandelbaum T, Scott DJ, Mark RG, Howell MD, Malhutra A, Talmor DS. Outcome of critically ill patients with acute kidney injury using the AKIN criteria. Poster presentation at the Critical Care Canada Forum 2010, November 7–10, 2010, Toronto, Canada, Nov. 2010.

[9]

Moody GB. The PhysioNet/Computing in Cardiology Challenge 2010: Mind the gap. Comput Cardiol, 37:305–308, Sept. 2010. (PDF) (PMID:21766058)

[10]

Ranger M, Heldt T, O'Leary H, Suleymanci M, Johnston C, du Plessis AJ. Description of global cerebral activation during noxious stimulus in critically ill preterm infants. Poster presentation, 8th International Workshop on Pediatric Pain, Mar. 2010.

[11]

Ranger M, Heldt T, O'Leary H, Suleymanci M, Johnston C, du Plessis AJ. Description of global cerebral activation during noxious stimulus in critically ill preterm infants. Poster contribution to the 5th International Workshop on Neonatal Brain Monitoring and Neuroprotection, Jan. 2010.

[12]

Silva I. PhysioNet 2010 Challenge: A robust multi-channel adaptive filtering approach to the estimation of physiological recordings. Comput Cardiol, 37:313–316, Sept. 2010. (PDF)

2009

[1]

Celi LA, Villarroel M, Clifford G, Szolovits P. Local customized mortality prediction modeling for patients with acute kidneyinjury admitted to the intensive care unit. Presentation at the Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Genova, Italy (http://cibb09.disi.unige.it), Oct. 2009.

[2]

Chen T, Clifford GD, Mark RG. The effect of signal quality on six cardiac output estimators. Comput Cardiol, 36:197–200, Sept. 2009. (PDF) (PMID:20740055)

[3]

Kashif FM, Heldt T, Novak V, Czosnyka M, Verghese GV. Non-invasive model-based cerebrovascular monitoring for neurotrauma. Poster presentation, CIMIT Innovation Congress 2009. (Awarded the “Most Innovative Research Award”), Oct. 2009.

[4]

Moody GB, Lehman LH. Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge. Comput Cardiol, 36:541–544, Sept. 2009. (PDF) (PMID:20842209)

2008

[1]

Clifford GD, Nemati S, Sameni R. An artificial multi-channel model for generating abnormal electrocardiographic rhythms. Comput Cardiol, 35:773–776, Sept. 2008. (PDF) (PMID:20808722)

[2]

Khaustov A, Nemati S, Clifford GD. An open-source standard T-wave alternans detector for benchmarking. Comput Cardiol, 35:509–512, Sept. 2008. (PDF) (PMID:20798786)

[3]

Lehman LH, Saeed M, Moody GB, Mark RG. Similarity-based searching in multi-parameter time series databases. Comput Cardiol, 35:653–656, Sept. 2008. (PDF) (PMID:21179377)

[4]

Li Q, Clifford GD. Suppression of false arrhythmia alarms from ICU monitors using heart rate estimation based on combined arterial blood pressure and ECG analysis. Shanghai, China, May 2008.

[5]

Moody GB. The PhysioNet/Computers in Cardiology Challenge 2008: T-wave alternans. Comput Cardiol, 35:505–508, Sept. 2008. (PDF) (PMID:19779602)

2007

[1]

Hug C, Clifford GD. An analysis of the errors in recorded heart rate and blood pressure in the ICU using a complex set of signal quality metrics. Comput Cardiol, 34:641–645, Sept. 2007. (PDF)

[2]

Jia X, Malhotra A, Talmor D, Saeed M, Mark RG. Risk factors for acute lung injury and acute respiratory distress syndrome in patients mechanically ventilated > 48 hours in the ICU. Presentation at the SSCM Critical Care Congress, Orlando FL, Feb. 2007.

[3]

Lehman LH, Kyaw TH, Clifford GD, Mark RG. A temporal search engine for a massive multi-parameter clinical information database. Comput Cardiol, 34:637–640, Sept. 2007. (PDF)

[4]

Parlikar TA, Heldt T, Ranade GV, Verghese GC. Model-based estimation of cardiac output and total peripheral resistance. Comput Cardiol, 34:379–382, 2007. (PDF)

[5]

Villarroel M, Saeed A, Clifford GD, Moody GB, Mark RG. Finding relevant cases in large databases of signals, time series, and clinical data. Comput Cardiol, 34:265–268, Sept. 2007. (PDF)

[6]

Wolfberg AJ, Syed Z, Clifford GD, Tin A, Guttag J, du Plessis AJ. Entropy of fetal EKG associated with intrapartum fever. Presented at the New England Conference on Perinatal Research, Oct. 2007. (PDF)

2006

[1]

Clifford GD, Villarroel M. Model-based determination of QT intervals. Comput Cardiol, 33:357–360, 2006. (PDF)

[2]

Clifford GD, Aboukhalil A, Zong W, Sun JX, Janz BA, Moody GB, Mark RG. Using the blood pressure waveform to reduce critical false ECG alarms. Comput Cardiol, 33:829–832, 2006. (PDF)

[3]

Heldt T, Chernyak YB. Analytical solution to minimal cardiovascular model. Comput Cardiol, 33:785–788, Sept. 2006. (PDF)

[4]

Heldt T, Long W, Verghese GC, Szolovits P, Mark RG. Integrating data, models, and reasoning in critical care. Conf Proc IEEE Eng Med Biol Soc. 2006, 1:350–353, Sept. 2006. (PDF) (doi:10.1109/IEMBS.2006.259734) (PMID:17946818)

[5]

Moody GB, Koch H, Steinhoff U. The PhysioNet/Computers in Cardiology Challenge 2006: QT interval measurement. Comput Cardiol, 33:313–316, 2006. (PDF) (doi:10.1109/CIC.2004.1442881)

[6]

Roberts JM, Parlikar TA, Heldt T, Verghese GC. Bayesian networks for cardiovascular monitoring. Proceedings of the 28th IEEE Engineering in Medicine and Biology Conference, 205–209, 2006. (PDF) (PMID:17946804)

[7]

Saeed M, Mark RG. A novel method for the efficient retrieval of similar multiparameter physiologic time series using Wavelet-based symbolic representations. AMIA Annu Symp Proc, 679–683, 2006. (PDF) (PMID:17238427)

[8]

Sun JX, Reisner AT, Mark RG. A signal abnormality index for arterial blood pressure waveforms. Comput Cardiol, 33:13–16, Sept. 2006. (PDF)

[9]

Zong W, Saeed M, Heldt T. A QT interval detection algorithm based on ECG curve length transform. Comput Cardiol, 33:377–380, 2006. (PDF)

2005

[1]

Clifford GD, McSharry PE. Method to filter ECGs and evaluate clinical parameter distortion using realistic ECG model parameter fitting. Comput Cardiol, 32:715–718, 2005. (PDF)

[2]

Clifford GD, Zapanta L, Janz BA, Mietus J, Mark RG. Segmentation of 24-hour cardiovascular activity using ECG-based sleep/sedation and noise metrics. Comput Cardiol, 32:595–598, 2005. (PDF)

[3]

Douglass M, Clifford GD, Reisner A, Long WJ, Moody GB, Mark RG. De-identification algorithm for free-text nursing notes. Comput Cardiol, 32:341–344, 2005. (PDF)

[4]

Heldt T, Mark RG. Understanding post-spaceflight orthostatic intolerance: a simulation study. Comput Cardiol, 32:631–634, 2005. (PDF)

[5]

Janz BA, Clifford GD, Mark RG. A multivariable analysis of sedation, activity and agitation in critically ill patients using the Riker scale, ECG, blood pressure and respiratory rate. Comput Cardiol, 32:735–738, 2005. (PDF)

[6]

Janz BA, Frassica J, Baker C, Clifford GD, Mark RG. A new paradigm for managing information in the ICU in response to the 80 hour work week. New England Surgical Society, 86:118–119, 2005. (PDF)

[7]

Janz BA, Saeed M, Frassica J, Clifford GD, Mark RG. Development and optimization of a Critical Care Alert and Display (CCAD) system using retrospective ICU databases. In AMIA Annu Symp Proc, volume 994, 2005. (PMID:16779281)

[8]

Janz BA, Saeed M, Frassica J, Clifford GD, Mark RG. Development and optimization of a critical care alert and display (CCAD) system using retrospective ICU databases. AMIA Annu Symp Proc, 2005. (PDF) (PMID:16779281)

[9]

McSharry PE, Clifford GD. A statistical model of the sleep-wake dynamics of the cardiac rhythm. Comput Cardiol, 32:591–594, 2005. (PDF)

[10]

Oefinger MB, Mark RG. A web-based tool for visualization and collaborative annotation of physiological databases. Comput Cardiol, 32, 2005. (PDF)

[11]

Oefinger MB, Krieger M, Mark RG. Long-term ECG trends in atherosclerotic mouse subjects. Comput Cardiol, 32:695–698, 2005. (PDF)

[12]

Parlikar TA, Verghese GC. A simple cycle-averaged model for cardiovascular dynamics. Proceedings of the 27th Annual IEEE Engineering in Medicine and Biology Society Conference, 27:5490–5494, 2005. (PDF) (PMID:17281496)

[13]

Saeed M, Janz B, Clifford GD, Abdala O, Kyaw T, Douglass M, Shu J, Reisner A, Long W, Szolovits P, Heldt T, Verghese G, Moody G, Mark. R. MIMIC II: A massive temporal database to support research in integrating data, models, and reasoning in critical care. AMIA conference, Oct. 2005, Washington DC., 2005.

[14]

Samar Z, Heldt T, Verghese GC, Mark RG. Model-based cardiovascular parameter estimation in the intensive care unit. Comput Cardiol, 32:635–638, 2005. (PDF)

[15]

Sun JX, Reisner AT, Saeed M, Mark RG. Estimating cardiac output from arterial blood pressure waveforms: a critical evaluation using the MIMIC II database. Comput Cardiol, 32:295–298, Sept. 2005. (PDF)

2004

[1]

Abdala OT, Saeed M. Estimation of missing values in clinical laboratory measurements of ICU patients using a weighted K-nearest neighbors algorithm. Comput Cardiol, 31:693–696, 2004. (PDF)

[2]

Abdala OT, Clifford GD, Saeed M, Reisner A, Moody GB, Henry I, Mark RG. The Annotation Station: an open-source technology for annotating large biomedical databases. Comput Cardiol, 31:681–685, 2004. (PDF)

[3]

Ali W, Eshelman L, Saeed M. Identifying artifacts in arterial blood pressure using morphogram variability. Comput Cardiol, 31:697–700, Sept. 2004. (PDF)

[4]

Clifford GD, McSharry PE. Generating 24-hour ECG, BP and respiratory signals with realistic linear and nonlinear clinical characteristics using a nonlinear model. Comput Cardiol, 31:709–712, 2004. (PDF)

[5]

Clifford GD, McSharry PE. A nonlinear artificial model for generating realistic correlated ECG, BP and respiration. 17th international EURASIP conference, 358–360, June 2004. Biosignal2004, Brno, Czech Republic. (PDF)

[6]

Clifford GD, McSharry PE. A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms. Proc of SPIE International Symposium on Fluctuations and Noise, 5467(34):290–301, 2004. (PDF)

[7]

Douglass M, Clifford GD, Reisner A, Moody GB, Mark RG. Computer-assisted de-identification of free text in the MIMIC II database. Comput Cardiol, 31:341–344, 2004. (PDF)

[8]

Healey J, Clifford GD, Kontothanassis L, McSharry PE. An open-source method for simulating atrial fibrillation using ECGSYN. Comput Cardiol, 31:425–427, 2004. (PDF)

[9]

Heldt T, Mark RG. Scaling cardiovascular parameters for population simulations. Comput Cardiol, 31:133–136, 2004. (PDF)

[10]

Jager F, Smrdel A, Mark RG. An open-source tool to evaluate performance of transient ST segment episode detection algorithms. Comput Cardiol, 31:585–588, 2004. (PDF)

[11]

McSharry PE, Clifford GD. A comparison of nonlinear noise reduction and independent component analysis using a realistic dynamical model of the electrocardiogram. Proc of SPIE International Symposium on Fluctuations and Noise, 5467(09):78–88, 2004. (PDF)

[12]

McSharry PE, Clifford GD. Open-source software for generating electrocardiogram signals. ARXIV preprints, 0406017, 2004. (PDF)

[13]

Moody GB. Spontaneous termination of atrial fibrillation: a challenge from PhysioNet and Computers in Cardiology 2004. Comput Cardiol, 31:101–104, Sept. 2004. (PDF) (doi:10.1109/CIC.2004.1442881)

[14]

Nam DS, Youn CH, Lee BH, Clifford GD, Healey J. QoS-constrained resource allocation for a Grid-based multiple source electrocardiogram application. Lecture Notes in Computer Science, 3043:352–359, 2004. Information Systems and Information Technologies (ISIT) Workshop, (Grid Session). (PDF)

[15]

Oefinger M, Moody GB, Krieger M, Mark RG. System for remote multi-channel real-time monitoring of ECG via the internet. Comput Cardiol, 31:753–756, 2004. (PDF)

[16]

Oefinger M, Zong W, Krieger M, Mark RG. An interactive web-based tool for multi-scale physiological data visualization. Comput Cardiol, 31:569–572, 2004. (PDF)

[17]

Shu J, Clifford GD, Saeed M, Long WJ, Moody GB, Szolovits P, Mark RG. An open-source, interactive Java-based system for rapid encoding of significant events in the ICU using the Unified Medical Language System. Comput Cardiol, 31:197–200, 2004. (PDF)

[18]

Wang H, Azuaje F, Clifford GD, Jung B, Black N. Methods and tools for generating and managing ecgML-based information. Comput Cardiol, 31:573–576, 2004. (PDF)

[19]

Youn CH, Kim B, Nam DS, Shim EB, Clifford GD, Healey J. Resource reconfiguration scheme based on temporal quorum status estimation in computational grids. Lecture Notes in Computer Science, 699–707, 2004. (PDF)

[20]

Youn CH, Nam DS, Kim B, An ES, Lee BH, Shim EB, Clifford GD. QoS quorum-constrained resource management in wireless grid. Lecture Notes in Computer Science, 3222:65–72, 2004. Network and Parallel Computing, (NPC 2004), IFIP International Conference, Wuhan, China, oct 18–20. (PDF)

2003

[1]

Chen JJS, Heldt T, Verghese GC, Mark RG. Analytical solution to simplified circulatory model using piecewise linear elastance. Comput Cardiol, 30:45–48, 2003. (PDF)

[2]

Heldt T, Chang JL, Verghese GC, Mark RG. Cycle-averaged models of cardiovascular dynamics. Modelling and Control in Biomedical Systems 2003, 387–392, 2003. (PDF)

[3]

Heldt T, Oefinger MB, Hoshiyama M, Mark RG. Circulatory response to passive and active changes in posture. Comput Cardiol, 30:263–266, Sept. 2003. (PDF)

[4]

Heldt T, Verghese GC, Kamm RD, Mark RG. Modeling cardiovascular response to gravitational stress–combined forward and inverse approach. In IFMBE Proceedings — World Congress on Medical Physics and Biomedical Engineering, 2003.

[5]

Moody GB, Jager F. Distinguishing ischemic from non-ischemic ST changes: the PhysioNet/Computers in Cardiology Challenge 2003. Comput Cardiol, 30:235–237, 2003. (PDF) (doi:10.1109/CIC.2003.1291134)

[6]

Moody GB, Dakin M, Mark RG. Web-enabled physiologic signal processing and analysis. Proc. World Congress on Medical Physics and Biomedical Engineering, 2003. (PDF)

[7]

Mukkamala R, Reisner AT, Hojman HM, Mark RG, Cohen RJ. Continuous cardiac output monitoring by peripheral blood pressure waveform analysis. Comput Cardiol, 30:255–258, 2003. (PDF)

[8]

Zong W, Heldt T, Moody GB, Mark RG. An open-source algorithm to detect onset of arterial blood pressure pulses. Comput Cardiol, 30:259–262, 2003. (PDF)

[9]

Zong W, Moody GB, Jiang D. A robust open-source algorithm to detect onset and duration of QRS complexes. Comput Cardiol, 30:737–740, 2003. (PDF)

Books and book chapters

2017

[1]

Celi LAG, Fraser HSF, Nikore V, Osorio JS, Paik K, editors. Global Health Informatics: Principles of eHealth and mHealth to improve quality of care. Cambridge: MIT Press; 2017.

2016

[1]

Lai Y, Salgueiro F, Stone D. Integrating Non-clinical Data with EHRs, In: MIT Critical Data [5]. 51–60. (doi:10.1007/978-3-319-43742-2)

[2]

Lokhandwala S, Rush B. Objectives of the Secondary Analysis of Electronic Health Record Data, In: MIT Critical Data [5]. 3–7. (doi:10.1007/978-3-319-43742-2)

[3]

Mark R. The Story of MIMIC. In MIT Critical Data , editor, Secondary Analysis of Electronic Health Records, 43–49. Springer International Publishing, Heidelberg, 1st ed., Sept. 2016. (doi:10.1007/978-3-319-43742-2)

[4]

Marshall J, Chahin A, Rush B. Review of Clinical Databases, In: MIT Critical Data [5]. 9–16. (doi:10.1007/978-3-319-43742-2)

[5]

MIT Critical Data , editor. Secondary Analysis of Electronic Health Records. 1st ed., Heidelberg: Springer International Publishing; 2016. (doi:10.1007/978-3-319-43742-2)

[6]

Nair S, Hsu D, Celi LA. Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data, In: MIT Critical Data [5]. 17–26. (doi:10.1007/978-3-319-43742-2)

[7]

Pollard T, Velasquez FDSFA. Data Preparation, In: MIT Critical Data [5]. 101–114. (doi:10.1007/978-3-319-43742-2)

[8]

Raffa JD, Ghassemi M, Naumann T, Feng M, Hsu D. Data Analysis, In: MIT Critical Data [5]. 205–261. (doi:10.1007/978-3-319-43742-2)

2015

[1]

Lehman LH, Johnson MJ, Nemati S, Adams RP, Mark RG. Bayesian nonparametric learning of switching dynamics in cohort physiological time series: Application in critical care patient monitoring. In Chen Z, editor, Advanced State Space Methods for Neural and Clinical Data, 257–282. Cambridge University Press, 2015.

2013

[1]

Heldt T, Verghese GC, Mark RG. Mathematical modeling of physiological systems. In Batzel JJ, Bachar M, Kappel F, editors, Mathematical Modeling and Validation in Physiology: Applications to the Cardiovascular and Respiratory Systems, Lecture Notes in Mathematics, chapter 2, 21–41. Springer Verlag, 2013. (PDF)

2011

[1]

Celi LA, Tang RJ, Villarroel MC, Davidzon G, Lester WT, Chueh HC. A clinical database-driven approach to decision support: Predicting mortality among patients with acute kidney injury. In Chyu MC, editor, Advances in Critical Care Engineering, chapter 10, 171–83. Multi-Science Publishing Co., Ltd., 2011.

2009

[1]

Clifford GD, Villarroel M, Scott DJ. User Guide and Documentation for the MIMIC II Database, Apr. 2009. Rev: 291 (2012-02-24). Available from: http://mimic.mit.edu.ezproxy.canberra.edu.au/archive/mimic-ii-guide.pdf.

2006

[1]

Clifford GD. Ch 3: ECG Statistics, Noise, Artifacts, and Missing Data in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 55–99.

[2]

Clifford GD. Ch 5: Linear Filtering Methods in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 135–170.

[3]

Clifford GD, Oefinger MB. Ch 2: ECG Acquisition, Storage, Transmission, and Representation in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 27–53.

[4]

Clifford GD, Azuaje F, McSharry PE, editors. Advanced Methods and Tools for ECG Analysis. 1st ed., Norwood, MA, USA: Artech House; 2006. (Engineering in Medicine and Biology; 1).

[5]

McSharry PE, Clifford GD. Ch 4: Models for ECG and RR interval Processes in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 101–133.

[6]

McSharry PE, Clifford GD. Ch 6: Nonlinear Filtering Methods in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 171–196.

[7]

Reisner AT, Clifford GD, Mark RG. Ch 1: The Physiological Basis of the Electrocardiogram in Advanced Methods and Tools for ECG Analysis. In Clifford GD, Azuaje F, McSharry PE, editors, Advanced Methods and Tools for ECG Analysis, number 1 in Engineering in Medicine and Biology, chapter 1, 1–25. Artech House, Norwood, MA, USA, 1st ed., Oct. 2006.

Theses

2015

[1]

Mulholland H. Understanding lactate in an intensive care setting. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2015. (PDF)

2009

[1]

Chen T. Cardiac output estimation from arterial blood pressure waveforms using the MIMIC II database. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2009. (PDF)

[2]

Deshmane AV. False arrhythmia alarm suppression using ECG, ABP, and photoplethysmogram. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2009. (PDF)

2007

[1]

Jia X. The effects of mechanical ventilation on the development of acute respiratory distress syndrome. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2007. (PDF)

[2]

Li SX. Probabilistic network models in cardiovascular monitoring. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[3]

Parlikar TA. Modeling and Monitoring of Cardiovascular Dynamics for Patients in Critical Care. Doctoral dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[4]

Saeed M. Temporal Pattern Recognition in Multiparameter ICU Data. Doctoral dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[5]

Shavdia D. Septic shock: Providing early warnings using logistic regression models. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2007. (PDF)

[6]

Zamanian SA. Modeling and simulating human cardiovascular response to acceleration. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

2006

[1]

Hug C. Predicting the risk and trajectory of intensive care patients using survival models. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[2]

Neamatullah I. Automated de-identification of free text medical records. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[3]

Oefinger MB. Monitoring Transient Repolarization Segment Morphology Deviations in Mouse ECG. Doctoral dissertation, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[4]

Roberts JM. Bayesian networks for cardiovascular monitoring. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2006. (PDF)

[5]

Sun JX. Cardiac output estimation using arterial blood pressure waveforms. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

2005

[1]

Abdala OT. The Annotation Station : an open source technology for data visualization and annotation of large biomedical databases. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[2]

Douglass M. Computer-assisted de-identification of free-text nursing notes. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Feb. 2005. (PDF)

[3]

Kyaw TH. Formatting and searching a massive, multi-parameter clinical information database. Master's thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[4]

Samar Z. Cardiovascular parameter estimation using a computational model. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2005. (PDF)

[5]

Shu J. Free text phrase encoding and information extraction from medical notes. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[6]

Zapanta LF. Heart rate variability in mice with coronary heart disease. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

2004

[1]

Heldt T. Computational Models of Cardiovascular Function During Orthostatic Stress. Doctoral dissertation, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2004. (PDF)

[2]

Thorn K. Characterization of intravenous medication administration in an intensive care unit. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2004. (PDF)

2003

[1]

Chen JJS. Analytical solution to a simplified circulatory model using piecewise linear elastance function. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, July 2003.

[2]

Oefinger MB. System for remote multichannel real-time monitoring of mouse ECG via the Internet. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2003.

[1]

Mitchell WG, Pande R, Robinson TE, Jones GD, Hou I, , Celi LA. The weekend effect for stroke patients admitted to intensive care: A retrospective cohort analysis. PLoS One, 15(6):e0234521, June PLoS One. (doi:10.1371/journal.pone.0234521) (PMID:32520977)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Impact of intensive care unit discharge delays on patient outcomes: A retrospective cohort study. J Intensive Care Med, 34(11–12):924–929, Nov. 2019. First published online: October 1, 2018. (doi:10.1177/0885066618800276) (PMID:30270722)

[2]

Bulgarelli L, Deliberato RO, Stone DJ, Celi LA, Johnson AEW. The authors reply. Crit Care Med, 47(7):e612, July 2019. (doi:10.1097/CCM.0000000000003786) (PMID:31205091)

[3]

Cosgriff CV, Celi LA, Ko S, Sundaresan T, de la Hoz MÁA, Kaufman AR, Stone DJ, Badawi O, Deliberato RO. Developing well-calibrated illness severity scores for decision support in the critically ill. NPJ Digit Med, 2:76, Aug. 2019. eCollection 2019. (doi:10.1038/s41746-019-0153-6) (PMID:31428687)

[4]

Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, Bonvini M, Celi LA, Douglas MJ. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients. NPJ Digit Med, 2:116, Nov. 2019. eCollection 2019. (doi:10.1038/s41746-019-0192-z) (PMID:31815192)

[5]

Deliberato RO, Escudero GG, Bulgarelli L, Serpa Neto A, Ko SQ, Campos NS, Saat B, Amaro Júnior E, Lopes FS, Johnson AEW. SEVERITAS: An externally validated mortality prediction for critically ill patients in low and middle-income countries. Int J Med Inform, 131:103959, Nov. 2019. Epub 2019 Sep 4. (doi:10.1016/j.ijmedinf.2019.103959) (PMID:31539837)

[6]

Deliberato RO, Neto AS, Komorowski M, Stone DJ, Ko SQ, Bulgarelli L, Ponzoni CR, de Freitas Chaves RC, Celi LA, Johnson AEW. An evaluation of the influence of body mass index on severity scoring. Crit Care Med, 47(2):247–253, Feb. 2019. (doi:10.1097/CCM.0000000000003528) (PMID:30395555)

[7]

Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng CY, Mark RG, Horng S. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data, 6(1):317, Dec. 2019. (doi:10.1038/s41597-019-0322-0) (PMID:31831740)

[8]

Naik GS, Waikar SS, Johnson AEW, Buchbinder EI, Haq R, Hodi FS, Schoenfeld JD, Ott PA. Complex inter-relationship of body mass index, gender and serum creatinine on survival: exploring the obesity paradox in melanoma patients treated with checkpoint inhibition. J Immunother Cancer, 7(1):89, Mar. 2019. (doi:10.1186/s40425-019-0512-5) (PMID:30922394)

[9]

Reiz AN, García MS, Sagasti FM, González MÁ, Malpica AB, Benítez JCM, Cabrera MN, del Pino Ramírez Á, Perdomo JMG, Alonso JP, Celi LA, de la Hoz MÁA, Deliberato R, Paik K, Pollard T, Raffa J, Torres F, Mayol J, Chafer J, Ferrer AG, Rey A, Luengo HG. Big data and machine learning in critical care: Opportunities for collaborative research. Med Intensiva, 43(1):52–57, Jan–Feb 2019. [Article in English, Spanish] Epub 2018 Aug 2. (doi:10.1016/j.medin.2018.06.002) (PMID:30077427)

[10]

Sandfort V, Johnson AEW, Kunz LM, Vargas JD, Rosing DR. Prolonged elevated heart rate and 90-day survival in acutely ill patients: Data from the MIMIC-III database. J Intensive Care Med, 34(8):622–629, Aug. 2019. (doi:10.1177/0885066618756828) (PMID:29402151)

[11]

Serpa Neto A, Deliberato RO, Johnson AEW, Pollard TJ, Celi LA, Pelosi P, Gama de Abreu M, Schultz MJ, PROVE Network Investigators . Normalization of mechanical power to anthropometric indices: impact on its association with mortality in critically ill patients. Intensive Care Med, 45(12):1835–1837, Dec. 2019. Epub 2019 Oct 8. (doi:10.1007/s00134-019-05794-9) (PMID:31595350)

[12]

Vistisen ST, Johnson AEW, Scheeren TWL. Predicting vital sign deterioration with artificial intelligence or machine learning, Dec. 2019. Published: 28 June 2019. (doi:10.1007/s10877-019-00343-7) (PMID:31254239)

Conference proceedings and presentations

[1]

Dauvin A, Donado C, Bachtiger P, Huang KC, Sauer CM, Ramazzotti D, Bonvini M, Celi LA, Douglas MJ. Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients, bringing context to abnormal admission lab values. Presented at the European Society of Intensive Care Medicine 32nd Annual Congress, Berlin (https://www.esicm.org/events/32nd-annual-congress-berlin/), Sept. 2019.

[2]

Lanius SM, Chen CW, Johnson AEW, Celi LA, Law AC. Outcome in dialyzed ICU patients in septic shock. In Am J Kidney Dis, volume 73, 693, May 2019. (doi:https://doi-org.ezproxy.canberra.edu.au/10.1053/j.ajkd.2019.03.199)

[3]

Raffa J, Johnson A, Celi LA, Pollard T, Pilcher D, Badawi O. The global open source severity of illness score (GOSSIS). Crit Care Med, 47(1):17, 2019. (doi:10.1097/01.ccm.0000550825.30295.dd)

[4]

Rincon T, Celi LA, Raffa J, Koestler D, Pollard T, Johnson A, Pierce J. Prognostic accuracy of the sofa score and a sepsis prompt in discriminating sepsis. Critical Care Medicine, 47(1):759, Jan. 2019. (doi:10.1097/01.ccm.0000552309.57308.ff)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Bose S, Johnson AEW, Moskowitz A, Celi LA, Raffa JD. Impact of Intensive Care Unit discharge delays on patient outcomes: A retrospective cohort study. J Intensive Care Med, 885066618800276, Oct. 2018. [Epub ahead of print]. (doi:10.1177/0885066618800276) (PMID:30270722)

[2]

Celi LA, Deliberato R, Vieira S. Foreword. Int J Med Inform, 113:96–97, May 2018. Epub 2018 Feb 23. (doi:10.1016/j.ijmedinf.2018.02.015) (PMID:29602439)

[3]

Deliberato RO, Ko S, Komorowski M, Armengol de La Hoz MÁ, Frushicheva MP, Raffa JD, Johnson AEW, Celi LA, Stone DJ. Severity of illness scores may misclassify critically ill obese patients. Crit Care Med, 46(3):394–400, Mar. 2018. (doi:10.1097/CCM.0000000000002868) (PMID:29194147)

[4]

Feng M, McSparron JI, Kien DT, Stone DJ, Roberts DH, Schwartzstein RM, Vieillard-Baron A, Celi LA. Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database. Intensive Care Med, 1–9, May 2018. [Epub ahead of print]. (doi:10.1007/s00134-018-5208-7) (PMID:29806057)

[5]

Johnson AEW, Aboab J, Raffa JD, Pollard TJ, Deliberato RO, Celi LA, Stone DJ. A comparative analysis of sepsis identification methods in an electronic database. Crit Care Med, Jan. 2018. [Epub ahead of print]. (doi:10.1097/CCM.0000000000002965) (PMID:29303796)

[6]

Lokhandwala S, McCague N, Chahin A, Escobar B, Feng M, Ghassemi MM, Stone DJ, Celi LA. One-year mortality after recovery from critical illness: A retrospective cohort study. PLoS ONE, 13(5):e0197226, May 2018. (doi:10.1371/journal.pone.0197226) (PMID:29750814)

[7]

Lyndon MP, P.Cassidy M, AnthonyCeli L, Hendrik L, Kim YJ, Gomez N, Baum N, Bulgarelli L, E.Paik K, Dagan A. Hacking Hackathons: Preparing the next generation for the multidisciplinary world of healthcare technology. Int J Med Inform, 112:1–5, Apr. 2018. Epub 2018 Jan 3. (doi:10.1016/j.ijmedinf.2017.12.020) (PMID:29500006)

[8]

Neto AS, Deliberato RO, Johnson AEW, Bos LD, Amorim P, Pereira SM, Cazati DC, Cordioli RL, Correa TD, Pollard TJ, et al. Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts. Intensive Care Med, 44(11):1914–1922, Nov. 2018. Epub 2018 Oct 5. (doi:10.1007/s00134-018-5375-6) (PMID:30291378)

[9]

Núñez Reiza A, de la Hoz MÁA, Sánchez García M. Big data analysis and machine learning in intensive care units. Med Intensiva, Dec. 2018. [Epub ahead of print] [Article in English, Spanish]. (doi:10.1016/j.medin.2018.10.007) (PMID:30591356)

[10]

Piza F, Celi LA, Deliberato RO, Bulgarelli L, de Carvalho FRT, Filho RR, Armengol de La Hoz MÁ, Kesselheim JC. Assessing team effectiveness and affective learning in a datathon. Int J Med Inform, 112:40–44, Apr. 2018. Epub 2018 Jan 11. (doi:10.1016/j.ijmedinf.2018.01.005) (PMID:29500020)

[11]

Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. Scientific Data, 5:180178, Sept. 2018. (doi:10.1038/sdata.2018.178) (PMID:30204154)

[12]

Pollard TJ, Johnson AEW, Raffa JD, Mark RG. tableone: An open source Python package for producing summary statistics for research papers. JAMIA Open, ooy012, May 2018. (doi:10.1093/jamiaopen/ooy012) (PMID:31984317)

[13]

Sauer CM, Sasson D, Paik KE, McCague N, Celi LA, Fernndez IS, Illigens BMW. Feature selection and prediction of treatment failure in tuberculosis. PLoS One, 13(11):e0207491, Nov. 2018. eCollection 2018. (doi:10.1371/journal.pone.0207491) (PMID:30458029)

[14]

Serpa Neto A, Kugener G, Bulgarelli L, Filho RR, Armengol de la Hoz MÁ, Johnson AEW, Paik KE, Torres F, Xie C, Jnior EA, Ferraz LJR, Celi LA, Deliberato RO. First Brazilian datathon in critical care. Rev Bras Ter Intensiva, 30(1):6–8, Jan–Mar 2018. (doi:10.5935/0103-507X.20180006) (PMID:29742215)

[15]

Severson KA, Ritter-Cox L, Raffa JD, Celi LA, Gordon WJ. Vasopressin administration is associated with rising serum lactate levels in patients with sepsis. J Intensive Care Med, 885066618794925, Aug. 2018. [Epub ahead of print]. (doi:10.1177/0885066618794925) (PMID:30130997)

[16]

Stretch R, Penna ND, Celi LA, Landon BE. Effect of boarding on mortality in ICUs. Crit Care Med, 46(4):525–531, Apr. 2018. (doi:10.1097/CCM.0000000000002905) (PMID:29252930)

[17]

Tyler PD, Du H, Feng M, Bai R, Xu Z, Horowitz GL, Stone DJ, Celi LA. Assessment of Intensive Care Unit laboratory values that differ from reference ranges and association with patient mortality and length of stay. JAMA Netw Open, 1(7):e184521, 2018. (doi:10.1001/jamanetworkopen.2018.4521) (PMID:30646358)

[18]

Vistisen ST, Moody B, Celi LA, Chen C. Post-extrasystolic characteristics in the arterial blood pressure waveform are associated with right ventricular dysfunction in intensive care patients. J Clin Monit Comput, Nov. 2018. [Epub ahead of print]. (doi:10.1007/s10877-018-0216-2) (PMID:30411186)

[19]

Zhu T, Johnson AEW, Yang Y, Clifford GD, Clifton DA. Bayesian fusion of physiological measurements using a signal quality extension. Physiological measurement, 39(6):065008, June 2018. (doi:10.1088/1361-6579/aac856) (PMID:29808824)

Conference proceedings and presentations

[1]

Angelotti G, Morandini P, Lehman L, Mark R, Barbieri R. The role of baroreflex sensitivity in acute hypotensive episodes prediction in the Intensive Care Unit. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2784–2787, July 2018. (PDF) (doi:10.1109/EMBC.2018.8512859)

[2]

Elahinia H, Raffa JD, Wu JT, Ghassemi MM. Predicting medical nonadherence using natural language processing. In 2017 IEEE MIT Undergraduate Research Technology Conference (URTC), Feb. 2018. (doi:10.1109/URTC.2017.8284212)

[3]

Ghassemi MM, Al-Hanai T, Raffa JD, Mark RG, Nemati S, Chokshi FH. How is the doctor feeling? ICU provider sentiment is associated with diagnostic imaging utilization. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4058–4064, July 2018. (PDF) (doi:10.1109/EMBC.2018.8513325)

[4]

Ghassemi MM, Moody BE, Lehman LH, Song C, Li Q, Sun H, Mark RG, Westover MB, Clifford GD. You snooze, you win: the PhysioNet/Computing in Cardiology Challenge 2018. Comput Cardiol, 45, Sept. 2018. (PDF) (doi:10.22489/CinC.2018.049)

[5]

Johnson AEW, Pollard TJ, Naumann T. Generalizability of predictive models for intensive care unit patients. In ML4H: Machine Learning for Health, Dec. 2018. Workshop at NeurIPS 2018.

[6]

Lehman EP, Krishnan RG, Zhao X, Mark RG, Lehman LwH. Representation learning approaches to detect false arrhythmia alarms from ECG dynamics. In Doshi-Velez F, Fackler J, Jung K, Kale D, Ranganath R, Wallace B, Wiens J, editors, Proceedings of the 3rd Machine Learning for Healthcare Conference, volume 85, 571–586, Palo Alto, California, 17–18 Aug 2018. PMLR. (PDF)

[7]

Ren O, Johnson AEW, Lehman EP, Komorowski M, Aboab J, Tang F, Shahn Z, Sow D, Mark R, Lehman L. Predicting and understanding unexpected respiratory decompensation in critical care using sparse and heterogeneous clinical data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI), 144–151, June 2018. (PDF) (doi:10.1109/ICHI.2018.00024)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Chen C, Lee J, Johnson AE, Mark RG, Celi LA, , Danziger J. Right ventricular function, peripheral edema, and acute kidney injury in critical illness. Kidney Int Rep, 2(6):1059–1065, June 2017. eCollection 2017 Nov. (doi:10.1016/j.ekir.2017.05.017) (PMID:29270515)

[2]

Clifford GD, Liu C, Moody B, Millet J, Schmidt S, Li Q, Silva I, Mark RG. Recent advances in heart sound analysis. Physiol Meas, 38(8):E10–E25, 2017. (doi:10.1088/1361-6579/aa7ec8) (PMID:28696334)

[3]

Deliberato RO, Celi LA, Stone DJ. Clinical note creation, binning, and artificial intelligence. JMIR Med Inform, 5(3):e24, 2017. (PDF) (doi:10.2196/medinform.7627) (PMID:28778845)

[4]

Deliberato RO, Rocha LL, Lima AH, Santiago CRM, Terra JCC, Dagan A, Celi LA. Physician satisfaction with a multi-platform digital scheduling system. PLoS ONE, 12(3):e0174127, 2017. (PDF) (doi:10.1371/journal.pone.0174127) (PMID:28328958)

[5]

Doocy S, Paik K, Lyles E, Tam HH, Fahed Z, Winkler E, Kontunen K, Mkanna A, Burnham G. Pilot testing and implementation of a mHealth tool for non-communicable diseases in a humanitarian setting. PLoS Curr,  9, June 2017. (doi:10.1371/currents.dis.e98c648aac93797b1996a37de099be74) (PMID:28744410)

[6]

Doocy S, Paik KE, Lyles E, Tam HH, Fahed Z, Winkler E, Kontunen K, Mkanna A, Burnham G. Guidelines and mHealth to improve quality of hypertension and type 2 diabetes care for vulnerable populations in Lebanon: Longitudinal cohort study. JMIR Mhealth Uhealth, 5(10):e158, Oct. 2017. (doi:10.2196/mhealth.7745) (PMID:29046266)

[7]

Fuchs L, Anstey M, Feng M, Toledano R, Kogan S, Howell MD, Clardy P, Celi L, Talmor D, Novack V. Quantifying the mortality impact of do-not-resuscitate orders in the ICU. Crit Care Med, 45(6):1019–1027, June 2017. (PDF) (doi:10.1097/CCM.0000000000002312) (PMID:28328651)

[8]

Fuchs L, Feng M, Novack V, Lee J, Taylor J, Scott D, Howell M, Celi L, Talmor D. The effect of ARDS on survival: do patients die from ARDS or with ARDS?. J Intensive Care Med, 885066617717659, July 2017. Epub ahead of print. (doi:10.1177/0885066617717659) (PMID:28681644)

[9]

Johnson AEW, Stone DJ, Celi LA, Pollard TJ. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc, Sept. 2017. (doi:10.1093/jamia/ocx084) (PMID:29036464)

[10]

Komorowski M, Celi LA. Will artificial intelligence contribute to overuse in healthcare?. Crit Care Med, 45(5):912–913, May 2017. (doi:10.1097/CCM.0000000000002351) (PMID:28410309)

[11]

Marshall DC, Salciccioli JD, Goodson RJ, Pimentel MA, Sun KY, Celi LA, Shalhoub J. The association between sodium fluctuations and mortality in surgical patients requiring intensive care. J Crit Care, 40:63–68, Aug. 2017. Epub 2017 Feb 13. (doi:10.1016/j.jcrc.2017.02.012) (PMID:28347943)

[12]

Moskowitz A, Chen KP, Cooper AZ, Chahin A, Ghassemi MM, Celi LA. Management of atrial fibrillation with rapid ventricular response in the intensive care unit: A secondary analysis of electronic health record data. Shock, 48(4):436–440, Oct. 2017. [Epub ahead of print]. (PMID:28328711)

[13]

Pathanasethpong A, Soomlek C, Morley K, Morley M, Polpinit P, Dagan A, Weis JW, Celi LA. Tackling regional public health issues using mobile health technology: Event report of an mHealth hackathon in Thailand. JMIR Mhealth Uhealth, 5(10):e155, Oct. 2017. (doi:10.2196/mhealth.8259) (PMID:29038098)

[14]

Pollard T, Celi LA. Enabling machine learning in critical care. ICU Management & Practice, 17(3), 2017. (PDF)

[15]

Rush B, Berger L, Celi LA. Access to palliative care for patients undergoing mechanical ventilation with idiopathic pulmonary fibrosis in the united states. Am J Hosp Palliat Care, 1049909117713990, June 2017. Epub ahead of print. (doi:10.1177/1049909117713990) (PMID:28602096)

[16]

Rush B, Martinka P, Kilb B, McDermid RC, Boyd JH, Celi LA. Acute respiratory distress syndrome in pregnant women. Obstet Gynecol, 129(3):530–535, 2017. (doi:10.1097/AOG.0000000000001907) (PMID:28178046)

[17]

Rush B, McDermid RC, Celi LA, Walley KR, Russell JA, H.Boyd J. Association between chronic exposure to air pollution and mortality in the acute respiratory distress syndrome. Environ Pollut, 224:352–356, May 2017. Epub 2017 Feb 13. (doi:10.1016/j.envpol.2017.02.014) (PMID:28202265)

[18]

Rush B, Walley KR, Celi LA, Rajoriya N, Brahmania M. Palliative care access for hospitalized patients with end-stage liver disease across the United States. Hepatology, June 2017. Epub ahead of print. (doi:10.1002/hep.29297) (PMID:28660622)

[19]

Rush B, Wiskar K, Celi LA, Walley KR, Russell JA, McDermid RC, Boyd JH. Association of household income level and in-hospital mortality in patients with sepsis: A nationwide retrospective cohort analysis. J Intensive Care Med, 885066617703338, Apr. 2017. Epub ahead of print. (doi:10.1177/0885066617703338) (PMID:28385107)

[20]

Tyler PD, Celi LA. Tele-ICU increases interhospital transfers: Does Big Brother know better?. Crit Care Med, 45(8):1417–1419, Aug. 2017. (doi:10.1097/CCM.0000000000002510) (PMID:28708685)

[21]

Wiens J, Snyder GM, Finlayson S, Mahoney MV, Celi LA. Potential adverse effects of broad-spectrum antimicrobial exposure in the Intensive Care Unit. Open Forum Infect Dis, 5(2):ofx270, Dec. 2017. eCollection 2018 Feb. (PDF) (doi:10.1093/ofid/ofx270) (PMID:29479546)

[22]

Wiskar K, Celi LA, Walley KR, Fruhstorfer C, Rush B. Inpatient palliative care referral and 9-month hospital readmission in patients with congestive heart failure: a linked nationwide analysis. J Intern Med, July 2017. Epub ahead of print. (doi:10.1111/joim.12657) (PMID:28741859)

[23]

Wiskar KJ, Celi LA, McDermid RC, Walley KR, Russell JA, Boyd JH, Rush B. Patterns of palliative care referral in patients admitted with heart failure requiring mechanical ventilation. Am J Hosp Palliat Care, 1049909117727455, Aug. 2017. Epub ahead of print. (doi:10.1177/1049909117727455) (PMID:28826226)

[24]

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, Doshi-Velez F. Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database. J Am Med Inform Assoc, 24(3):488–495, May 2017. (doi:10.1093/jamia/ocw138) (PMID:27707820)

Conference proceedings and presentations

[1]

Clifford G, Liu C, Moody B, Silva I, Li Q, Johnson A, Mark R. AF classification from a short single lead ECG recording: the PhysioNet Computing in Cardiology Challenge 2017. Comput Cardiol, 44, 2017. In press.

[2]

Dai Y, Lokhandwala S, Long W, Mark R, Lehman LH. Phenotyping hypotensive patients in critical care using hospital discharge summaries. Proc IEEE Intl Conf Biomed Health Inform, 2017. (PDF)

[3]

Ghassemi MM, Jarvis W, Alhanai T, Brown EN, Mark RG, Westover MB. An open-source tool for the transcription of paper-spreadsheet data. In 2017 IEEE International Conference on Big Data (Big Data), 935–941, Dec. 2017. (PDF)

[4]

Johnson AEW, Mark RG. Real-time mortality prediction in the Intensive Care Unit. In AMIA Annu Symp Proc 2017, 994–1003, Nov. 2017. eCollection 2017. (PDF)

[5]

Johnson AEW, Pollard TJ, Mark RG. Reproducibility in critical care: a mortality prediction case study. In Doshi-Velez F, Fackler J, Kale D, Ranganath R, Wallace B, Wiens J, editors, Proceedings of the 2nd Machine Learning for Healthcare Conference, volume 68, 361–376, Boston, Massachusetts, 18–19 Aug 2017. PMLR. (PDF)

[6]

Lehman L, Johnson A, Sudduth C, Mark R, Nemati S. Dynamics of multivariate vital sign time series and severe sepsis among patients in critical care. J Crit Care, 38:365, Apr. 2017. (doi:10.1016/j.jcrc.2016.11.021)

[7]

Zalewski A, Long W, Johnson AEW, Mark RG, Lehman LH. Estimating patient’s health state using latent structure inferred from clinical time series and text. Proc IEEE Intl Conf Biomed Health Inform, 2017. (PDF)

Books and book chapters

[1]

Celi LAG, Fraser HSF, Nikore V, Osorio JS, Paik K, editors. Global Health Informatics: Principles of eHealth and mHealth to improve quality of care. Cambridge: MIT Press; 2017.

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Aboab J, Celi LA, Charlton P, Feng M, Ghassemi M, Marshall DC, Mayaud L, Naumann T, McCague N, Paik KE, Pollard TJ, Resche-Rigon M, Salciccioli JD, Stone DJ. A datathon model to support cross-disciplinary collaboration. Science Translational Medicine, 8(333):333ps8, Apr. 2016. (PDF) (doi:10.1126/scitranslmed.aad9072) (PMID:27053770)

[2]

Angelidis P, Berman L, Casas-Perez ML, Celi LA, Dafoulas GE, Dagan A, Escobar B, Lopez DM, Noguez J, Osorio-Valencia JS, Otine C, Paik K, Rojas-Potosi L, Symeonidis AL, Winkler E. The hackathon model to spur innovation around global mHealth. J Med Eng Technol, 10:1–8, Aug. 2016. [Epub ahead of print]. (doi:10.1080/03091902.2016.1213903) (PMID:27538360)

[3]

Boone MD, Massa J, Mueller A, Jinadasa SP, Lee J, Kothari R, Scott DJ, Callahan J, Celi LA, Hacker MR. The organizational structure of an intensive care unit influences treatment of hypotension among critically ill patients: A retrospective cohort study. J Crit Care, pii: S0883–9441(16)00067–8, Feb. 2016. [Epub ahead of print]. (doi:10.1016/j.jcrc.2016.02.009) (PMID:26975737)

[4]

Celi LA, Lokhandwala S, Montgomery R, Moses C, Naumann T, Pollard T, Spitz D, Stretch R. Datathons and software to promote reproducible research. J Med Internet Res, 18(8):e230, Aug. 2016. (doi:10.2196/jmir.6365) (PMID:27558834)

[5]

Celi LA, Davidzon G, Johnson AEW, Komorowski M, Marshall DC, Nair SS, Phillips CT, Pollard TJ, Raffa JD, Salciccioli JD, Salgueiro FM, Stone DJ. Bridging the health data divide. J Med Internet Res, 18(12):e325, Dec. 2016. (PDF) (doi:10.2196/jmir.6400) (PMID:27998877)

[6]

Chen KP, Cavender S, Lee J, Feng M, Mark RG, Celi LA, Mukamal KJ, Danziger J. Peripheral edema, central venous pressure, and risk of AKI in critical illness. Clin J Am Soc Nephrol, 11(4):602–8, Apr. 2016. Epub 2016 Jan 19. (PDF) (doi:10.2215/CJN.08080715) (PMID:26787777)

[7]

Clifford GD, Silva I, Moody B, Li Q, Kella D, Chahin A, Kooistra T, Perry D, Mark RG. False alarm reduction in critical care. Physiol Meas, 37(8):E5–E23, Aug. 2016. Epub 2016 Jul 25. (PDF) (doi:10.1088/0967-3334/37/8/E5) (PMID:27454172)

[8]

Danziger J, Chen K, Cavender S, Lee J, Feng M, Mark RG, Mukamal KJ, Celi LA. Admission peripheral edema, central venous pressure, and survival in critically ill patients. Ann Am Thorac Soc, 13(5):705–11, May 2016. First published online 11 Mar 2016. (doi:10.1513/AnnalsATS.201511-737OC) (PMID:26966784)

[9]

Danziger J, Chen K, Lee J, Feng M, Mark RG, Celi L, Mukamal KJ. Obesity, acute kidney injury, and mortality in critical illness. Crit Care Med, 44(2):328–34, Feb. 2016. [Epub ahead of print]. (PDF) (doi:10.1097/CCM.0000000000001398) (PMID:26496453)

[10]

DePasse J, Celi LA. Collaboration, capacity building and co-creation as a new mantra in global health. Int J Qual Health Care, 28(4):536–7, Sept. 2016. Epub 2013 Nov 13. (doi:10.1093/intqhc/mzt077) (PMID:24225268)

[11]

Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine learning and decision support in critical care. Proceedings of the IEEE, 104(2):444–466, Feb. 2016. (PDF) (doi:10.1109/JPROC.2015.2501978) (PMID:27765959)

[12]

Johnson AEW, Pollard TJ, Shen L, Lehman LH, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG. MIMIC-III, a freely accessible critical care database. Sci Data, 3:160035, May 2016. Published online 24 May 2016. (PDF) (doi:10.1038/sdata.2016.35) (PMID:27219127)

[13]

Katz DS, Niemeyer KE, Smith AM, Anderson WL, Boettiger C, Hinsen K, Hooft R, Hucka M, Lee A, Löffler F, Pollard T, Rios F. Software vs. data in the context of citation. PeerJ Preprints, 4:e2630v1, Dec. 2016. (PDF) (doi:10.7287/peerj.preprints.2630v1)

[14]

Lee J, Mark RG, Celi LA, Danziger J. Proton pump inhibitors are not associated with acute kidney injury in critical illness. J Clin Pharmacol, 56(12):1500–1506, Dec. 2016. [Epub ahead of print]. (doi:10.1002/jcph.805) (PMID:27492273)

[15]

Lehman LH, Mark RG, Nemati S. A model-based machine learning approach to probing autonomic regulation from nonstationary vital-signs time series. IEEE J Biomed Health Inform, PP(99):1, Dec. 2016. [Epub ahead of print]. (PDF) (doi:10.1109/JBHI.2016.2636808) (PMID:28114047)

[16]

Liu C, Springer D, Li Q, Moody B, Juan RA, Chorro FJ, Castells F, Roig JM, Silva I, Johnson AEW, Syed Z, Schmidt SE, Papadaniil CD, Hadjileontiadis L, Naseri H, Moukadem A, Dieterlen A, Brandt C, Tang H, Samieinasab M, Samieinasab MR, Sameni R, Mark RG, Clifford GD. An open access database for the evaluation of heart sound algorithms. Physiol Meas, 37(12):2181–2213, Dec. 2016. Epub 2016 Nov 21. (PMID:27869105)

[17]

Lynch KE, Ghassemi F, Flythe JE, Feng M, Ghassemi M, Celi LA, Brunelli SM. Sodium modelling to reduce intradialytic hypotension during haemodialysis for acute kidney injury in the intensive care unit. Nephrology, 21(10):870–877, Oct. 2016. First published: 12 September 2016. (doi:10.1111/nep.12677) (PMID:26590371)

[18]

Moskowitz A, Lee J, Donnino MW, Mark R, Celi LA, Danziger J. The association between admission magnesium concentrations and lactic acidosis in critical illness. J Intensive Care Med, 31(3):187–92, Apr. 2016. [Epub 2014 Apr 14]. (PDF) (doi:10.1177/0885066614530659) (PMID:24733810)

[19]

Naidus E, Celi LA. Big data in healthcare: are we close to it?. Rev Bras Ter Intensiva, 28(1):8–10, Mar. 2016. (PDF) (PMID:27096670)

[20]

Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, da Veiga Leprevost F, Fufezan C, Ternent T, Eglen SJ, Katz DS, Pollard TJ, Konovalov A, Flight RM, Blin K, Vizcaino JA. Ten simple rules for taking advantage of git and GitHub. PLoS Comput Biol, 12(7):e1004947, July 2016. eCollection 2016. (PDF) (doi:10.1371/journal.pcbi.1004947) (PMID:27415786)

[21]

Pimentel MAF, Brennan T, Lehman L, King NKK, Ang B, Feng M. Outcome prediction for patients with traumatic brain injury with dynamic features from intracranial pressure and arterial blood pressure signals: A gaussian process approach. Acta Neurochir Suppl, 122:85–91, 2016. (PDF) (doi:10.1007/978-3-319-22533-3_17) (PMID:27165883)

[22]

Rush B, Hertz P, Bond A, McDermid R, Celi LA. Utilization of palliative care in patients with end-stage chronic obstructive pulmonary disease on home oxygen: national trends and barriers to care in the United States. Chest, pii: S0012–3692(16)52413–1, July 2016. [Epub ahead of print]. (doi:10.1016/j.chest.2016.06.023) (PMID:27387892)

[23]

Rush B, Romano K, Ashkanani M, McDermid RC, Celi LA. Impact of hospital case-volume on subarachnoid hemorrhage outcomes: A nationwide analysis adjusting for hemorrhage severity. J Crit Care, pii: S0883–9441(16)30513–5, Sept. 2016. [Epub ahead of print]. (doi:10.1016/j.jcrc.2016.09.009) (PMID:27663296)

[24]

Shrime MG, Ferket BS, Scott DJ, Lee J, Barragan-Bradford D, Pollard T, Arabi YM, Al-Dorzi HM, Baron RM, Hunink MGM, Celi LA, Lai PS. Time-limited trials of intensive care for critically ill patients with cancer: How long is long enough?. JAMA Oncol., 2(1):76–83, Jan. 2016. [Published online October 15, 2015.]. (PDF) (doi:10.1001/jamaoncol.2015.3336) (PMID:26469222)

[25]

Stupple A, Geocadin RG, Celi LA. Conversation prior to resuscitation: The new CPR. Resuscitation, 99:e3, Feb. 2016. Published online 2015 Dec 29. (doi:10.1016/j.resuscitation.2015.12.006) (PMID:26740412)

[26]

Van Poucke S, Zhang Z, Schmitz M, Vukicevic M, Vander Laenen M, Celi LA, Deyne CD. Scalable predictive analysis in critically ill patients using a visual open data analysis platform. PLoS One, 11(1):e0145791, 2016. Published online 2016 Jan 5. (doi:10.1371/journal.pone.0145791) (PMID:26731286)

[27]

Wu M, Ghassemi M, Feng M, Celi LA, Szolovits P, Doshi-Velez F. Understanding vasopressor intervention and weaning: Risk prediction in a public heterogeneous clinical time series database. J Am Med Inform Assoc, pii: ocw138, Oct. 2016. [Epub ahead of print]. (doi:10.1093/jamia/ocw138) (PMID:27707820)

Conference proceedings and presentations

[1]

Adibuzzaman M, Musselman K, Johnson A, Brown P, Pitluk Z, Grama A. Closing the data loop: An integrated open access analysis platform for the MIMIC database. Comput Cardiol, 43:205–208, 2016. (PDF) (doi:10.22489/CinC.2016.043-205)

[2]

Bonvini M, Kaufman A, Ramazzotti D, Celi LA, Stretch R. Comparison of imputation methods to predict baseline serum creatinine. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000509965.53628.fb)

[3]

Bose S, Moskowitz A, Jalilian L, Celi LA, Johnson AEW. Impact of intensive care unit discharge delays. Am J Respir Crit Care Med, 193:A4695–A4695, 2016. Presentation at the American Thoracic Society 2016 International Conference, May 13–18, 2016, San Francisco.

[4]

Chen C, Celi LA. Left ventricular diastolic dysfunction and hospital mortality. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508844.84122.82)

[5]

Clifford GD, Liu C, Moody B, Springer D, Silva I, Li Q, Mark RG. Classification of normal/abnormal heart sound recordings: the PhysioNet/Computing in Cardiology Challenge 2016. Comput Cardiol, 43:609–612, 2016. (PDF)

[6]

Della Penna N, Stretch R, Celi LA. Mortality heterogeneity of geographic co-localization of intensive care unit patient and care team. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000509819.41485.8f)

[7]

Johnson A, Celi LA, Raffa J, Pollard T, Ston D. External validation of the sepsis-3 guidelines. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508736.93826.b5)

[8]

Lehman LH, Johnson A, Sudduth C, Mark R, Nemati S. Dynamics of multivariate vital sign time series and severe sepsis among patients in critical care. Presented at the 15th International Conference on Complex Acute Illness Conference (ICCAI), Pasadena, California, USA, Aug. 2016.

[9]

Marshall JD, You CX, Pollard T, Salgueiro F, Chen C, Celi LA. Impact of left ventricular heart failure with preserved ejection fraction and right ventricular systolic heart failure on outcomes in the intensive care unit. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[10]

Pacheco R, Salgado C, Deliberato R, Celi LA, Sousa J, Vieira S. Modeling to individualize mean arterial pressure threshold to prevent acute kidney injury in the ICU. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000508810.24919.2c)

[11]

Pollard T. Crowdsourcing research communities to solve problems in critical care. Oral presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[12]

Pollard TJ. An introduction to the MIMIC-III Critical Care Database. Presented at the London Critical Care Datathon (http://datascicc.org/), Dec. 2016. (PDF)

[13]

Pollard T, Komorowski M, Salciccioli JD, Marshall DC, Sykes M, Goodson R, Hartley A, Shalhoub J. Lactate rebound as an independent predictor of mortality in the intensive care unit. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016. (PDF)

[14]

Raffa JD, Montgomery RA, Stretch R, Johnson AEW, Celi LA, Pollard T. Trends in mechanical ventilation and vasopressor use and relevance to mortality outcomes in critical care settings. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[15]

Sun F, Cui A, Lokhandwala S, Tyler P, Shen M, Paul D, Pollard T. Risk factors for mortality in critically ill patients requiring new renal replacement therapy. Poster discussion presentation at the American Thoracic Society International Conference, San Francisco, May 13–18, 2016.

[16]

Tyler P, Celi LA, Rush B. Interhospital transfer of patients with sepsis across the United States. Presentation at the 46th Annual Critical Care Congress, January 21–25, 2017, Honolulu, Hawaii, USA, Dec. 2016. (doi:10.1097/01.ccm.0000510016.10287.46)

Books and book chapters

[1]

Lai Y, Salgueiro F, Stone D. Integrating Non-clinical Data with EHRs, In: MIT Critical Data [5]. 51–60. (doi:10.1007/978-3-319-43742-2)

[2]

Lokhandwala S, Rush B. Objectives of the Secondary Analysis of Electronic Health Record Data, In: MIT Critical Data [5]. 3–7. (doi:10.1007/978-3-319-43742-2)

[3]

Mark R. The Story of MIMIC. In MIT Critical Data , editor, Secondary Analysis of Electronic Health Records, 43–49. Springer International Publishing, Heidelberg, 1st ed., Sept. 2016. (doi:10.1007/978-3-319-43742-2)

[4]

Marshall J, Chahin A, Rush B. Review of Clinical Databases, In: MIT Critical Data [5]. 9–16. (doi:10.1007/978-3-319-43742-2)

[5]

MIT Critical Data , editor. Secondary Analysis of Electronic Health Records. 1st ed., Heidelberg: Springer International Publishing; 2016. (doi:10.1007/978-3-319-43742-2)

[6]

Nair S, Hsu D, Celi LA. Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data, In: MIT Critical Data [5]. 17–26. (doi:10.1007/978-3-319-43742-2)

[7]

Pollard T, Velasquez FDSFA. Data Preparation, In: MIT Critical Data [5]. 101–114. (doi:10.1007/978-3-319-43742-2)

[8]

Raffa JD, Ghassemi M, Naumann T, Feng M, Hsu D. Data Analysis, In: MIT Critical Data [5]. 205–261. (doi:10.1007/978-3-319-43742-2)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Badawi O, Brennan T, Celi LA, Feng M, Ghassemi M, Ippolito A, Johnson A, Mark RG, Mayaud L, Moody G, Moses C, Naumann T, Nikore V, Pimentel M, Pollard TJ, Santos M, Stone DJ, Zimolzak A. Metadata correction: making big data useful for health care: a summary of the inaugural MIT critical data conference. JMIR Med Inform, 3(1):e6, Jan. 2015. Correction to the article Making Big Data Useful for Health Care: A Summary of the Inaugural MIT Critical Data Conference in volume 2, e22. (doi:10.2196/medinform.4226) (PMID:25608565)

[2]

Byamba K, Syed-Abdul S, Garca-Romero MT, Huang CW, Nergyi S, Nyamdorj A, Nguyen PA, Iqbal U, Paik K, Celi LA, Nikore V, Somai M, Jian WS, Li YC. Mobile teledermatology for a prompter and more efficient dermatological care in rural Mongolia. Br J Dermatol, 173(1):265–7, July 2015. Epub 2015 May 12. (doi:10.1111/bjd.13607) (PMID:25494968)

[3]

Celi LA, Marshall JD, Lai Y, Stone DJ. Disrupting electronic health records systems: The next generation. JMIR Med Inform, 3(4):e34, Oct. 2015. (doi:10.2196/medinform.4192) (PMID:26500106)

[4]

Chen KP, Lee J, Mark RG, Feng M, Celi LA, Malley BE, Danziger J. Proton pump inhibitor use is not associated with cardiac arrhythmia in critically ill patients. J Clin Pharmacol, 55(7):774–9, July 2015. Epub 2015 Mar 16. (PDF) (doi:10.1002/jcph.479) (PMID:25655574)

[5]

de Louw EJ, Sun PO, Lee J, Feng M, Mark RG, Celi LA, Mukamal KJ, Danziger J. Increased incidence of diuretic use in critically ill obese patients. J Crit Care, 30(3):619–23, June 2015. Epub 2015 Feb 7. (PDF) (doi:10.1016/j.jcrc.2015.01.023) (PMID:25721030)

[6]

Ghassemi M, Celi LA, Stone DJ. State of the art review: the data revolution in critical care. Crit Care, 19(1):118, Mar. 2015. (doi:10.1186/s13054-015-0801-4) (PMID:25886756)

[7]

Ghosh S, Feng M, Nguyen H, Li J. Hypotension risk prediction via sequential contrast patterns of icu blood pressure. IEEE J Biomed Health Inform, July 2015. [Epub ahead of print]. (PDF) (PMID:26168449)

[8]

Hsu DJ, Feng M, Kothari R, Zhou H, Chen KP, Celi LA. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: A propensity score analysis. Chest, 148(6):1470–1476, Aug. 2015. [Epub ahead of print]. (PDF) (doi:10.1378/chest.15-0516) (PMID:26270005)

[9]

Lee J, de Louw E, Niemi M, Nelson R, Mark RG, Celi LA, Mukamal KJ, Danziger J. Association between fluid balance and survival in critically ill patients. J Intern Med, 277(4):468–77, Apr. 2015. Epub 2014 Jun 27. (PDF) (doi:10.1111/joim.12274) (PMID:24931482)

[10]

Lehman LH, Adams RP, Mayaud L, Moody GB, Malhotra A, Mark RG, Nemati S. A physiological time series dynamics-based approach to patient monitoring and outcome prediction. IEEE J Biomed Health Inform, 19(3):1068–1076, May 2015. [Epub 2014 Jun 30]. (PDF) (doi:10.1109/JBHI.2014.2330827) (PMID:25014976)

[11]

Minhas MA, Velasquez AG, Kaul A, Salinas PD, Celi LA. Effect of protocolized sedation on clinical outcomes in mechanically ventilated intensive care unit patients: A systematic review and meta-analysis of randomized controlled trials. Mayo Clin Proc, 90(5):613–23, May 2015. Epub 2015 Apr 9. (doi:10.1016/j.mayocp.2015.02.016) (PMID:25865475)

[12]

Morgado E, Alonso-Atienza F, Santiago-Mozos R, Barquero-Pérez Ó, Silva I, Ramos J, Mark R. Quality estimation of the electrocardiogram using crosscorrelation among leads. BioMed Eng OnLine, 15:59, 2015. (PDF) (doi:10.1186/s1293801500531) (PMID:26091857)

[13]

Moskowitz A, McSparron J, Stone DJ, Celi LA. Preparing a new generation of clinicians for the era of big data. Harv Med Stud Rev, 2(1):24–27, Jan. 2015. (PMID:25688383)

[14]

Paonessa JR, Brennan T, Pimentel M, Steinhaus D, Feng M, Celi LA. Hyperdynamic left ventricular ejection fraction in the intensive care unit. Crit Care, 19:288, 2015. (PDF) (doi:10.1186/s13054-015-1012-8) (PMID:26250903)

[15]

Pereira RDMA, Salgado CM, Dejam A, Reti SR, Vieira SM, Sousa JMC, Celi LA, Finkelstein SN. Fuzzy modeling to predict severely depressed left ventricular ejection fraction following admission to the intensive care unit using clinical physiology. The Scientific World Journal, 2015. Article ID 212703, 9 pages. (PDF) (doi:10.1155/2015/212703) (PMID:26345130)

[16]

Salciccioli JD, Marshall DC, Pimentel MAF, Santos MD, Pollard T, Celi LA, Shalhoub J. The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study. Crit Care, 19:13, Jan. 2015. (doi:10.1186/s13054-014-0731-6) (PMID:25598149)

[17]

Shaw ND, Butler JP, Nemati S, Kangarloo T, Ghassemi M, Malhotra A, Hall JE. Accumulated deep sleep is a powerful predictor of lh pulse onset in pubertal children. J Clin Endocrinol Metab, 100(3):1062–1070, Mar. 2015. [First Published Online: December 09, 2014.]. (PDF) (doi:10.1210/jc.2014-3563) (PMID:25490277)

[18]

Silva I, Moody B, Behar J, Johnson A, Oster J, Clifford GD, Moody GB. Robust detection of heart beats in multimodal data. Physiol Meas, 36(8):1629–44, Aug. 2015. [Epub 2015 Jul 28]. (doi:10.1088/0967-3334/36/8/1629) (PMID:26217894)

[19]

Stone DJ, Celi LA, Csete M. Engineering control into medicine. J Crit Care, 30(3):652.e1–e7, June 2015. Epub 2015 Jan 30. (doi:10.1016/j.jcrc.2015.01.019) (PMID:25680579)

[20]

Wyber R, Vaillancourt S, Perry W, Mannava P, Folaranmic T, Celi LA. Big data in global health: improving health in low- and middle-income countries. Bull World Health Organ, 93(3):203–8, Mar. 2015. Epub 2015 Jan 30. (doi:10.2471/BLT.14.139022) (PMID:25767300)

Conference proceedings and presentations

[1]

Chronaki C, Shahin A, Mark R. Designing reliable cohorts of cardiac patients across MIMIC and eICU. Comput Cardiol, 42:189–192, 2015. (PDF)

[2]

Clifford G, Silva I, Moody B, Li Q, Kella D, Shahin A, Kooistra T, Perry D, Mark R. The PhysioNet/Computing in Cardiology Challenge 2015: Reducing false arrhythmia alarms in the ICU. Comput Cardiol, 42:273–276, 2015. (PDF)

[3]

Ghassemi MM, Amorim E, Pati SB, Mark RG, Brown EN, Purdon PL, Westover MB. An enhanced cerebral recovery index for coma prognostication following cardiac arrest. Conf Proc IEEE Eng Med Biol Soc, 2015:534–7, 2015. (PDF) (PMID:26736317)

[4]

Ghassemi M, Pimentel MA, Naumann T, Brennan T, Clifton DA, Szolovits P, Fengr M. A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data. Proc Conf AAAI Artif Intell, 2015:446–453, Jan. 2015. (PMID:27182460)

[5]

Ghassemi MM, Mark RG, Nemati S. A visualization of evolving clinical sentiment using vector representations of clinical notes. Comput Cardiol, 42:629–632, 2015. (PDF) (PMID:27774487)

[6]

Lehman LH, Ghassemi M, Snoek J, Nemati S. Patient prognosis from vital sign time series: Combining convolutional neural networks with a dynamical systems approach. Comput Cardiol, 42:1069–1072, 2015. (PDF)

[7]

Lehman LH, Nemati S, Mark RG. Hemodynamic monitoring using switching autoregressive dynamics of multivariate vital sign time series. Comput Cardiol, 42:1065–1068, 2015. (PDF)

[8]

Pollard T, Komorowski M, Johnson A, Salciccioli J. Critical care datathon: Answering clinically relevant questions with the mimic critical care datase. Presentation at the Mozilla Festival, 2015, Nov. 2015.

Books and book chapters

[1]

Lehman LH, Johnson MJ, Nemati S, Adams RP, Mark RG. Bayesian nonparametric learning of switching dynamics in cohort physiological time series: Application in critical care patient monitoring. In Chen Z, editor, Advanced State Space Methods for Neural and Clinical Data, 257–282. Cambridge University Press, 2015.

Theses

[1]

Mulholland H. Understanding lactate in an intensive care setting. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2015. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Badawi O, Brennan T, Celi LA, Feng M, Ghassemi M, Ippolito A, Johnson A, Mark RG, Mayaud L, Moody G, Moses C, Naumann T, Pimentel M, Pollard TJ, Santos M, Stone DJ, Zimolzak A. Making big data useful for health care: A summary of the inaugural MIT critical data conference. JMIR Med Inform, 2(2):e22, 2014. (PDF) (doi:10.2196/medinform.3447) (PMID:25600172)

[2]

Boone MD, Celi LA, Ho BG, Pencina M, Curry MP, Lior Y, Talmor D, Novack V. Model for end-stage liver disease score predicts mortality in critically ill cirrhotic patients. J Crit Care, 29(5):881.e7–13, Oct. 2014. Epub 2014 May 28. (doi:10.1016/j.jcrc.2014.05.013) (PMID:24974049)

[3]

Celi LA, Csete M, Stone D. Optimal data systems: the future of clinical predictions and decision support. Curr Opin Crit Care, 20(5):573–80, Oct. 2014. (doi:10.1097/MCC.0000000000000137) (PMID:25137399)

[4]

Celi LA, Ippolito A, Montgomery RA, Moses C, Stone DJ. Crowdsourcing knowledge discovery and innovations in medicine. J Med Internet Res, 16(9):e216, June 2014. (PDF) (doi:10.2196/jmir.3761) (PMID:25239002)

[5]

Celi LA, Moseley E, Moses C, Ryan P, Somai M, Stone D, Tang K. From pharmacovigilance to clinical care optimization. Big Data, 2(3):134–141, Sept. 2014. Online ahead of print: August 13, 2014. (PDF) (doi:10.1089/big.2014.0008) (PMID:26576325)

[6]

Celi LA, Zimolzak AJ, Stone DJ. Dynamic clinical data mining: Search engine-based decision support. JMIR Med Inform, 2(1):e13, 2014. (PDF) (doi:10.2196/medinform.3110) (PMID:25600664)

[7]

Clifford GD, Silva I, Behar J, Moody GB. Non-invasive fetal ECG analysis. Physiol Meas, 35(8):1521–36, Aug. 2014. Epub 2014 Jul 29. (PDF) (doi:10.1088/0967-3334/35/8/1521) (PMID:25071093)

[8]

Dejam A, Malley BE, Feng M, Cismondi F, Park S, Samani S, Samani ZA, Pinto DS, Celi LA. The effect of age and clinical circumstances on the outcome of red blood cell transfusion in critically ill patients. Critical Care, 18(4):487, 2014. [Epub ahead of print]. (PDF) (doi:10.1186/s13054-014-0487-z) (PMID:25175389)

[9]

Fuchs L, Novack V, McLennan S, Celi LA, Baumfeld Y, Park S, Howell MD, Talmor DS. Trends in severity of illness on icu admission and mortality among the elderly. PLoS One, 9(4):e93234, Apr. 2014. (PDF) (doi:10.1371/journal.pone.0093234) (PMID:24699251)

[10]

Ghassemi M, Marshall J, Singh N, Stone DJ, Celi LA. Leveraging a critical care database: selective serotonin reuptake inhibitor use prior to ICU admission is associated with increased hospital mortality. Chest, 145(4):745–752, Apr. 2014. (doi:10.1378/chest.13-1722) (PMID:24371841)

[11]

Ghassemi MM, Richter SE, Eche IM, Chen TW, Danziger J, Celi LA. A data-driven approach to optimized medication dosing: a focus on heparin. Intensive Care Med, online publication, Aug. 2014. (PDF) (doi:10.1007/s00134-014-3406-5) (PMID:25091788)

[12]

Moseley ET, Hsu DJ, Stone DJ, Celi LA. Beyond open big data: Addressing unreliable research. J Med Internet Res, 16(11):e259, 2014. (PDF) (doi:10.2196/jmir.3871) (PMID:25405277)

[13]

Silva I, Moody G. An open-source toolbox for analysing and processing physionet databases in MATLAB and octave. Journal of Open Research Software, 2(1):e27, 2014. (PDF) (doi:10.5334/jors.bi) (PMID:26525081)

[14]

Velasquez A, Ghassemi M, Szolovits P, Park S, Osorio J, Dejam A, Celi L. Long-term outcomes of minor troponin elevations in the intensive care unit. Anaesth Intensive Care, 42(3):356–64, May 2014. (PDF) (PMID:24794476)

Conference proceedings and presentations

[1]

Ghassemi M, Lehman LH, Snoek J, Nemati S. Global optimization approaches for parameter tuning in biomedical signal processing: A focus of multi-scale entropy. Comput Cardiol, 41:993–996, 2014. (PDF)

[2]

Ghosh S, Feng M, Nguyen H, Li J. Predicting heart beats using co-occurring constrained sequential patterns. Comput Cardiol, 41:265–268, 2014. (PDF)

[3]

Lehman LH, Long W, Saeed M, Mark RG. Latent topic discovery of clinical concepts from hospital discharge summaries of a heterogeneous patient cohort. Proceedings of the 36th International Conference of the IEEE Engineering in Medicine and Biology Society, 1773–1776, Aug. 2014. (PDF) (doi:10.1109/EMBC.2014.6943952)

[4]

Lehman LH, Nemati S, Moody G, Heldt T, Mark RG. Uncovering clinical significance of vital sign dynamics in critical care. Comput Cardiol, 41:1141–1144, 2014. (PDF)

[5]

Moody GB, Moody B, Silva I. Robust detection of heart beats in multimodal data: the PhysioNet/Computing in Cardiology Challenge 2014. Comput Cardiol, 41:549–552, 2014. (PDF)

[6]

Naumann T, Silva I. Scaling the WFDB Toolbox for MATLAB and Octave. Comput Cardiol, 41:161–164, 2014. (PDF)

[7]

Springer DB, Brennan T, Hitzeroth J, Mayosi BM, Tarassenko L, Clifford GD. Robust heart rate estimation from noisy phonocardiograms. Comput Cardiol, 41:613–616, 2014. (PDF)

[8]

Zhang Z, Ghassemi M, Silva I, Ainslie P, Celi LA, Cheng GZ. Modeling circadian rhythm variations during sepsis. Am J Respir Crit Care Med, B105. SEPSIS: CARE MODELS AND OUTCOMES:A3795, May 2014.

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Celi LA, Scott DJ, Lee J, Nelson R, Mukamal K, Mark R, Danziger J. Association of hypermagnesemia and blood pressure in the critically ill. J Hypertens, 31(11):2136–2141, Nov. 2013. discussion 2141. (PDF) (doi:10.1097/HJH.0b013e3283642f18) (PMID:24029865)

[2]

Celi LA, Mark RG, Stone DJ, Montgomery RA. Big Data in the Intensive Care Unit. Am J Respir Crit Care Med, 187(11):1157–1160, June 2013. (PDF) (doi:10.1164/rccm.201212-2311ED) (PMID:23725609)

[3]

Cismondi F, Celi LA, Fialho AS, Vieira SM, Reti SR, Sousa JMC, Finkelstein SN. Reducing unnecessary lab testing in the ICU with artificial intelligence. Int J Med Inform, 82(5):345–58, May 2013. [Epub 2012 Dec 28]. (PDF) (PMID:23273628)

[4]

Danziger J, William JH, Scott DJ, Lee J, Lehman L, Mark RG, Howell MD, Celi LA, Mukamal KJ. Proton-pump inhibitor use is associated with low serum magnesium concentrations. Kidney International, 83(4):692–699, Apr. 2013. Published online 16 January 2013. (PDF) (doi:10.1038/ki.2012.452) (PMID:23325090)

[5]

Fialho AS, Celi LA, Cismondi F, Vieira SM, Reti SR, Sousa JMC, Finkelstein SN. Disease-based modeling to predict fluid response in intensive care units. Methods Inf Med, 52(5), Aug. 2013. [Epub ahead of print]. (PDF) (PMID:23986268)

[6]

Fuchs L, Lee J, Novack V, Baumfeld Y, Scott D, Celi L, Mandelbaum T, Howell M, Talmor D. Severity of acute kidney injury and two-year outcomes in critically ill patients. Chest, 144(3):866–875, 2013. [Epub ahead of print]. (doi:10.1378/chest.12-2967) (PMID:23681257)

[7]

Lee J, Govindan S, Celi LA, Khabbaz KR, Subramaniam B. Customized prediction of short length of stay following elective cardiac surgery in elderly patients using a genetic algorithm. World J Cardiovasc Surg, 3(5):163–170, Sept. 2013. (PDF) (PMID:24482754)

[8]

Lehman LH, Saeed M, Talmor D, Mark R, Malhotra A. Methods of blood pressure measurement in the ICU. Crit Care Med, 41(1):34–40, Jan. 2013. (doi:10.1097/CCM.0b013e318265ea46) (PMID:23269127)

[9]

Mandelbaum T, Lee J, Scott DJ, Mark RG, Malhotra A, Howell MD, Talmor D. Empirical relationships among oliguria, creatinine, mortality, and renal replacement therapy in the critically ill. Intensive Care Med, 39(3):414–419, Dec. 2013. (T. Mandelbaum and J. Lee contributed equally to this work. Published online 7 December 2012.). (PDF) (doi:10.1007/s00134-012-2767-x) (PMID:23223822)

[10]

Mayaud L, Lai PS, Clifford GD, Tarrasenko L, Celi LA, Annane D. Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension. Crit Care Med, 41(4):954–962, Apr. 2013. (PMID:23385106)

[11]

Moses C, Celi LA, Marshall J. Pharmacovigilance: An active surveillance system to proactively identify risks for adverse events. Popul Health Manag, 16(3):147–9, June 2013. (doi:10.1089/pop.2012.0100) (PMID:23530466)

[12]

Perry W, Kwok A, Kozycki C, Celi LA. Disparities in end-of-life care: A perspective and review of quality. Popul Health Manag, 16(2):71–3, Apr. 2013. Epub 2013 Feb 13. (doi:10.1089/pop.2012.0061) (PMID:23405874)

[13]

Scott DJ, Lee J, Silva I, Park S, Moody GB, Celi LA, Mark RG. Accessing the public MIMIC-II intensive care relational database for clinical research. BMC Med Inform Decis Mak, 13:9, Jan. 2013. (PDF) (doi:10.1186/1472-6947-13-9) (PMID:23302652)

Conference proceedings and presentations

[1]

Lehman LH, Nemati S, Adams RP, Moody G, Malhotra A, Mark RG. Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series. Conf Proc IEEE Eng Med Biol Soc, 7072–5, 2013. (PDF) (doi:10.1109/EMBC.2013.6611187) (PMID:24111374)

[2]

Moody GB. LightWAVE: Waveform and annotation viewing and editing in a web browser. Comput Cardiol, 40:17–20, 2013. (PDF)

[3]

Nemati S, Lehman LH, Adams RP. Learning outcome-discriminative dynamics in multivariate physiological cohort time series. Conf Proc IEEE Eng Med Biol Soc, 7104–7, 2013. (PDF) (doi:10.1109/EMBC.2013.6611195) (PMID:24111382)

[4]

Silva I, Behar J, Sameni R, Zhu T, Oster J, Clifford GD, Moody GB. Noninvasive fetal ECG: the PhysioNet/Computing in Cardiology Challenge 2013. Comput Cardiol, 40:149–152, 2013. (PDF) (PMID:25401167)

Books and book chapters

[1]

Heldt T, Verghese GC, Mark RG. Mathematical modeling of physiological systems. In Batzel JJ, Bachar M, Kappel F, editors, Mathematical Modeling and Validation in Physiology: Applications to the Cardiovascular and Respiratory Systems, Lecture Notes in Mathematics, chapter 2, 21–41. Springer Verlag, 2013. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Celi LA, Galvin S, Davidzon G, Lee J, Scott D, Mark R. A database-driven decision support system: Customized mortality prediction. J Pers Med, 2(4):138–148, Sept. 2012. (doi:10.3390/jpm2040138) (PMID:23766893)

[2]

Celi LAG, Lee J, Scott DJ, Panch T, Mark RG. Collective experience: a database-fuelled, inter-disciplinary team-led learning system. J Comput Sci Eng, 6(1):51–59, 2012. (PDF) (doi:10.5626/JCSE.2012.6.1.51) (PMID:23766887)

[3]

Clifford GD, Moody GB. Signal quality in cardiorespiratory monitoring. Physiol Meas, 33(9), 2012. Focus issue: signal quality in cardiorespiratory monitoring. Gari D Clifford and George B Moody, Guest Editors. (PDF) (doi:10.1088/0967-3334/33/9/E01)

[4]

Fuchs L, Chronaki CE, Park S, Novack V, Baumfeld Y, Scott D, McLennan S, Talmor D, Celi L. ICU admission characteristics and mortality rates among elderly and very elderly patients. Intensive Care Med, 2012. Published online 15 July 2012. (PDF) (doi:10.1007/s00134-012-2629-6) (PMID:22797350)

[5]

Hunziker S, Celi LA, Lee J, Howell MD. Red cell distribution width improves the saps score for risk prediction in unselected critically ill patients. Crit Care, 16:R89, 2012. (PDF) (doi:10.1186/cc11351) (PMID:22607685)

[6]

Lee J, Kothari R, Ladapo JA, Scott DJ, Celi LA. Interrogating a clinical database to study treatment of hypotension in the critically ill. BMJ Open, 2012(2):e000916, 2012. (PDF) (doi:10.1136/bmjopen-2012-000916) (PMID:22685222)

[7]

Lee J, Nemati S, Silva I, Edwards BA, Butler JP, Malhotra A. Transfer entropy estimation and directional coupling change detection in biomedical time series. BioMed Eng OnLine, 11:19, 2012. (PDF) (doi:10.1186/1475-925X-11-19) (PMID:22500692)

[8]

Silva I, Lee J, Mark RG. Signal quality estimation with multi-channel adaptive filtering in intensive care settings. IEEE Trans Biomed Eng, 59(9):2476–85, Sept. 2012. Epub 2012 Jun 14. (PDF) (PMID:22717504)

Conference proceedings and presentations

[1]

Berg KM, Ghassemi M, Donnino MW, Marshall J, Celi L. Pre-admission use of selective serotonin reuptake inhibitors is associated with icu mortality. Poster presentation [Poster Board #224] at the American Thoracic Society International Conference, San Francisco, May 18–23, 2012.

[2]

Lehman L, Nemati S, Adams RP, Mark R. Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU. Conf Proc IEEE Eng Med Biol Soc. 2012, 5939–42, 2012. (PDF) (PMID:23367281)

[3]

Lehman L, Saeed M, Long W, Lee J, Mark R. Risk stratification of ICU patients using topic models inferred from unstructured progress notes. AMIA Annu Symp Proc, 505–11, Nov. 2012. (PDF) (PMID:23304322)

[4]

Nemati S, Lehman L, Adams RP, Malhotra A. Discovering shared cardiovascular dynamics within a patient cohort. Proc 34th IEEE EMBS, 2012. (PDF)

[5]

Silva I, Moody GB, Scott DJ, Celi LA, Mark RG. Predicting in-hospital mortality of ICU patients: the PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol, 39:245–248, 2012. (PDF) (PMID:24678516)

[6]

Silva I, Moody G, Scott DJ, Celi LA, Mark RG. Predicting in-hospital mortality of ICU patients: The PhysioNet/Computing in Cardiology Challenge 2012. Comput Cardiol, 39:245–248, 2012. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Celi LAG, Tang RJ, Villaroel M, Davidzon GA, Lester WT, Chueh HC. A clinical database-driven approach to decision support: Predicting mortality among patients with acute kidney injury. Journal of Healthcare Engineering, 2(1):97–110, Mar. 2011. (PMID:22844575)

[2]

Hug C, Clifford GD, Reisner AT. Clinician blood pressure documentation of stable intensive care patients: an intelligent archiving agent has a higher association with future hypotension. Crit Care Med, 39(5):1006–1014, May 2011. [Epub ahead of print]. (PDF) (doi:10.1097/CCM.0b013e31820eab8e) (PMID:21336136)

[3]

Mandelbaum T, Scott DJ, Lee J, Mark RG, Malhotra A, Waikar S, Howell MD, Talmor DS. Outcome of critically ill patients with acute kidney injury using the Acute Kidney Injury Network criteria. Crit Care Med, 39(12):2659–2664, Dec. 2011. Preprint available online 14 July 2011. (PMID:21765352)

[4]

Nemati S, Abdala O, Bazan V, Tim-Yeh S, Malhotra A, Clifford GD. A non-parametric surrogate-based test of significance for T-wave alternans detection. IEEE Transactions On Biomedical Engineering, 58(5):1356–64, May 2011. Epub Apr 19, 2010. (PDF) (doi:10.1109/TBME.2010.2047859) (PMID:20409986)

[5]

Nemati S, Malhotra A, Clifford GD. T-wave alternans patterns during sleep in healthy, cardiac disease, and sleep apnea patients. J Electrocardiol, 44(2):126–30, Mar–Apr 2011. Epub Dec 15, 2010. (PDF) (doi:10.1016/j.jelectrocard.2010.10.036) (PMID:21163493)

[6]

Saeed M, Villarroel M, Reisner AT, Clifford G, Lehman L, Moody G, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access intensive care unit database. Crit Care Med, 39(5):952–960, 2011. (PDF) (doi:10.1097/CCM.0b013e31820a92c6) (PMID:21283005)

Conference proceedings and presentations

[1]

Lee J, Scott DJ, Villarroel M, Clifford GD, Saeed M, Mark RG. Open-access MIMIC-II database for intensive care research. Conf Proc IEEE Eng Med Biol Soc. 2011, 8315–8318, 2011. (PDF) (PMID:22256274)

[2]

Mandelbaum T, Scott DJ, Lee J, Mark RG, Howell MD, Malhotra A, Talmor D. Validation of the AKIN criteria definition using high-resolution ICU data from the MIMIC-II database. Critical Care, 15(Suppl 1):105, 2011. (doi:10.1186/cc9525)

[3]

Moody BE. A rule-based method for ECG quality control. Comput Cardiol, 38:361–363, 2011. (PDF)

[4]

Moody GB, Mark RG, Goldberger AL. PhysioNet: physiologic signals, time series, and related open source software for basic, clinical, and applied research. Proc 33rd IEEE EMBS, 8327–8330, 2011. (PDF) (PMID:22256277)

[5]

Silva I, Lee J, Mark RG. Photoplethysmograph quality estimation through multichannel filtering. Conf Proc IEEE Eng Med Biol Soc. 2011, 4361–4364, 2011. (PDF) (PMID:22255305)

[6]

Silva I, Moody G, Celi L. Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011. Comput Cardiol, 38:273–276, 2011. (PDF)

Books and book chapters

[1]

Celi LA, Tang RJ, Villarroel MC, Davidzon G, Lester WT, Chueh HC. A clinical database-driven approach to decision support: Predicting mortality among patients with acute kidney injury. In Chyu MC, editor, Advances in Critical Care Engineering, chapter 10, 171–83. Multi-Science Publishing Co., Ltd., 2011.

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Campana LM, Owens RL, Clifford GD, Pittman SD, Malhotra A. Phase rectified signal averaging as a sensitive index of autonomic changes with aging. J Appl Physiol, 108(6):1668–1673, June 2010. E-print ahead of publication: March 25, 2010. (PDF) (doi:10.1152/japplphysiol.00013.2010) (PMID:20339014)

[2]

Clifford GD, Nemati S, Sameni R. An artificial vector model for generating abnormal electrocardiographic rhythms. Physiol Meas, 31(5):595–609, May 2010. IOP 'Featured Article'. (PDF) (doi:10.1088/0967-3334/31/5/001) (PMID:20308774)

[3]

Heldt T, Mukkamala R, Moody GB, Mark RG. CVSim: an open-source cardiovascular simulator for teaching and research. The Open Pacing, Electrophysiology, and Therapy Journal, 3:45–54, 2010. (PDF) (doi:10.2174/1876536X01003010045) (PMID:21949555)

[4]

Lee J, Mark RG. An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomed Eng Online, 9:62, Oct. 2010. (PDF) (doi:10.1186/1475-925X-9-62) (PMID:20973998)

[5]

Monasterio V, Clifford GD, Laguna P, Martínez JP. A multilead scheme based on periodic component analysis for T wave alternans analysis in the ECG. Ann Biomed Eng, 38(8):2532–2541, Aug. 2010. (PDF) (doi:10.1007/s10439-010-0029-z) (PMID:20387121)

[6]

Nemati S, Malhotra A, Clifford GD. Data fusion for improved respiration rate estimation. EURASIP Journal on Advances in Signal Processing, 2010(926305):1–10, May 2010. (PDF) (doi:10.1155/2010/926305) (PMID:20806056)

[7]

Sayadi O, Shamsollahi MB, Clifford GD. Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Transactions on Biomedical Engineering, 57(2):353–362, Feb. 2010. (PDF) (doi:10.1109/TBME.2009.2031243) (PMID:19758851)

[8]

Sayadi O, Shamsollahi MB, Clifford GD. Synthetic ECG generation and Bayesian filtering using a Gaussian wave-based dynamical model. Physiol Meas, 31(10):1309–29, Oct. 2010. Epub Aug 18, 2010. (PDF) (PMID:20720288)

[9]

Silva I, Epstein M. Estimating loudness growth from tone-burst evoked responses. J Acoust Soc Am, 127(6):3629–3642, 2010. (PDF) (doi:10.1121/1.3397457) (PMID:20550262)

Conference proceedings and presentations

[1]

Celi LA, Hug C, Villarroel M, Clifford G, Mark R. Issues with data mining: predictive modeling on critically ill patients who develop acute renal failure. Crit Care Med, Jan. 2010.

[2]

Celi LA, Villarroel M, Davidzon G, Galvin S, Clifford G, Galvin I, Bunton R, Szolovits P. Comparing the performance of customized mortality prediction models using local database against current standard scoring systems. Crit Care Med, Jan. 2010.

[3]

Craig M, Moody B, Jia S, Villarroel M, Mark R. Matching data fragments with imperfect identifiers from disparate sources. Comput Cardiol, 37:793–796, Sept. 2010. (PDF)

[4]

Kashif FM, Heldt T, Novak V, Czosnyka M, Verghese GV. Model-based cerebrovascular monitoring. Oral contribution, American Heart Association 2010 International Stroke Conference, Feb. 2010.

[5]

Lee J, Mark RG. A hypotensive episode predictor for intensive care based on heart rate and blood pressure time series. Comput Cardiol, 37:81–84, Sept. 2010. (PDF)

[6]

Lehman L, Saeed M, Moody GB, Mark RG. Hypotension as a risk factor for acute kidney injury in ICU patients. Comput Cardiol, 37:1095–1098, Sept. 2010. (PDF)

[7]

Lojun SL, Sauper CJ, Medow M, Long WJ, Mark RG, Barzilay R. Investigating resuscitation code assignment in the intensive care unit using structured and unstructured data. AMIA Annu Symp Proc, 2010:467–471, 2010. (PDF) (PMID:21347022)

[8]

Mandelbaum T, Scott DJ, Mark RG, Howell MD, Malhutra A, Talmor DS. Outcome of critically ill patients with acute kidney injury using the AKIN criteria. Poster presentation at the Critical Care Canada Forum 2010, November 7–10, 2010, Toronto, Canada, Nov. 2010.

[9]

Moody GB. The PhysioNet/Computing in Cardiology Challenge 2010: Mind the gap. Comput Cardiol, 37:305–308, Sept. 2010. (PDF) (PMID:21766058)

[10]

Ranger M, Heldt T, O'Leary H, Suleymanci M, Johnston C, du Plessis AJ. Description of global cerebral activation during noxious stimulus in critically ill preterm infants. Poster presentation, 8th International Workshop on Pediatric Pain, Mar. 2010.

[11]

Ranger M, Heldt T, O'Leary H, Suleymanci M, Johnston C, du Plessis AJ. Description of global cerebral activation during noxious stimulus in critically ill preterm infants. Poster contribution to the 5th International Workshop on Neonatal Brain Monitoring and Neuroprotection, Jan. 2010.

[12]

Silva I. PhysioNet 2010 Challenge: A robust multi-channel adaptive filtering approach to the estimation of physiological recordings. Comput Cardiol, 37:313–316, Sept. 2010. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Celi LA, Sarmenta L, Rotberg J, Marcelo A, Clifford GD. Mobile care (Moca) for remote diagnosis and screening. Journal of Health Informatics in Developing Countries, 3(1):17–21, 2009. (PDF) (PMID:21822397)

[2]

Clifford GD, Long WJ, Moody GB, Szolovits P. Robust parameter extraction for decision support using multimodal intensive care data. Phil Trans Royal Soc A, 367(1877):411–429, Jan. 2009. Special issue on Signal Processing in Vital Rhythms and Signs. (PDF) (doi:10.1098/rsta.2008.0157) (PMID:18936019)

[3]

Li Q, Mark RG, Clifford GD. Artificial arterial blood pressure artifact models and an evaluation of a robust blood pressure and heart rate estimator. Biomed Eng Online, 8(13), July 2009. (doi:10.1186/1475-925X-8-13) (PMID:19586547)

[4]

Moody GB. Physionet: Research resource for complex physiologic signals. [Japanese Journal of Electrocardiology], 29:1–3, 2009. (PDF)

[5]

Sun JX, Reisner AT, Saeed M, Heldt T, Mark RG. The cardiac output from blood pressure algorithms trial. Crit Care Med, 37(1):72–80, Jan. 2009. (PDF) (PMID:19112280)

Conference proceedings and presentations

[1]

Celi LA, Villarroel M, Clifford G, Szolovits P. Local customized mortality prediction modeling for patients with acute kidneyinjury admitted to the intensive care unit. Presentation at the Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Genova, Italy (http://cibb09.disi.unige.it), Oct. 2009.

[2]

Chen T, Clifford GD, Mark RG. The effect of signal quality on six cardiac output estimators. Comput Cardiol, 36:197–200, Sept. 2009. (PDF) (PMID:20740055)

[3]

Kashif FM, Heldt T, Novak V, Czosnyka M, Verghese GV. Non-invasive model-based cerebrovascular monitoring for neurotrauma. Poster presentation, CIMIT Innovation Congress 2009. (Awarded the “Most Innovative Research Award”), Oct. 2009.

[4]

Moody GB, Lehman LH. Predicting acute hypotensive episodes: The 10th annual PhysioNet/Computers in Cardiology Challenge. Comput Cardiol, 36:541–544, Sept. 2009. (PDF) (PMID:20842209)

Books and book chapters

[1]

Clifford GD, Villarroel M, Scott DJ. User Guide and Documentation for the MIMIC II Database, Apr. 2009. Rev: 291 (2012-02-24). Available from: http://mimic.mit.edu.ezproxy.canberra.edu.au/archive/mimic-ii-guide.pdf.

Theses

[1]

Chen T. Cardiac output estimation from arterial blood pressure waveforms using the MIMIC II database. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2009. (PDF)

[2]

Deshmane AV. False arrhythmia alarm suppression using ECG, ABP, and photoplethysmogram. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2009. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Aboukhalil A, Nielsen L, Saeed M, Mark RG, Clifford GD. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform. J Biomed Inform, 41(3):442–451, June 2008. (doi:10.1016/j.jbi.2008.03.003) (PMID:18440873)

[2]

Clifford GD, Blaya JA, Hall-Clifford R, Fraser HSF. Medical information systems: A foundation for healthcare technologies in developing countries. BMC Biomed Eng Online, 7(1):18, 2008. (doi:10.1186/1475-925X-7-18) (PMID:18547411)

[3]

Dawoud F, Wagner G, Moody G, Horácek B. Using inverse electrocardiography to image myocardial infarction–reflecting on the 2007 PhysioNet/Computers in Cardiology Challenge. J Electrocardiol, 41(6):630–5, 2008. (PMID:18954610)

[4]

Jia X, Malhotra A, Saeed M, Mark RG, Talmor D. Risk factors for Acute Respiratory Distress Syndrome in patients mechanically ventilated for > 48 h. Chest, 133(4):853–861, Apr. 2008. (doi:10.1378/chest.07-1121) (PMID:18263691)

[5]

Li Q, Mark RG, Clifford GD. Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. IOP Physiol Meas, 29(1):15–32, Jan. 2008. (Awarded the Martin Black Prize for Best Paper in Physiological Measurement in 2008). (PDF) (PMID:18175857)

[6]

Neamatullah I, Douglass M, Lehman LH, Reisner A, Villarroel M, Long WJ, Szolovits P, Moody GB, Mark RG, Clifford GD. Automated de-identification of free-text medical records. BMC Med Inform Decis Mak, 8:32, July 2008. (PDF) (doi:10.1186/1472-6947-8-32) (PMID:18652655)

[7]

Wolfberg AJ, DeRosier DJ, Roberts T, Syed Z, Clifford GD, Acker D, du Plessis AJ. A comparison of subjective and mathematical estimations of fetal heart rate variability. Journal of Maternal-Fetal and Neonatal Medicine, 21(2):101–4, 2008. (doi:10.1080/14767050701836792) (PMID:18240077)

Conference proceedings and presentations

[1]

Clifford GD, Nemati S, Sameni R. An artificial multi-channel model for generating abnormal electrocardiographic rhythms. Comput Cardiol, 35:773–776, Sept. 2008. (PDF) (PMID:20808722)

[2]

Khaustov A, Nemati S, Clifford GD. An open-source standard T-wave alternans detector for benchmarking. Comput Cardiol, 35:509–512, Sept. 2008. (PDF) (PMID:20798786)

[3]

Lehman LH, Saeed M, Moody GB, Mark RG. Similarity-based searching in multi-parameter time series databases. Comput Cardiol, 35:653–656, Sept. 2008. (PDF) (PMID:21179377)

[4]

Li Q, Clifford GD. Suppression of false arrhythmia alarms from ICU monitors using heart rate estimation based on combined arterial blood pressure and ECG analysis. Shanghai, China, May 2008.

[5]

Moody GB. The PhysioNet/Computers in Cardiology Challenge 2008: T-wave alternans. Comput Cardiol, 35:505–508, Sept. 2008. (PDF) (PMID:19779602)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Lian J, Clifford GD, Müessig D, Lang V. Open source model for generating RR intervals in atrial fibrillation and beyond. BioMedical Engineering OnLine, 6(9):1–16, Mar. 2007. doi:10.1186/1475-925X-6-9. (PDF) (PMID:17335580)

[2]

Sameni R, Clifford GD, Shamsollahi MB, Jutten C. Multi-channel ECG and noise modeling: application to maternal and fetal ECG signals. EURASIP Journal on Advances in Signal Processing, 2007(43407):1–14, 2007. (PDF) (doi:10.1155/2007/43407)

[3]

Sameni R, Shamsollahi MB, Jutten C, Clifford GD. A nonlinear Bayesian filtering framework for ECG denoising. IEEE Trans Biomed Eng, 54(12):2172–2185, Dec. 2007. (PDF) (PMID:18075033)

Conference proceedings and presentations

[1]

Hug C, Clifford GD. An analysis of the errors in recorded heart rate and blood pressure in the ICU using a complex set of signal quality metrics. Comput Cardiol, 34:641–645, Sept. 2007. (PDF)

[2]

Jia X, Malhotra A, Talmor D, Saeed M, Mark RG. Risk factors for acute lung injury and acute respiratory distress syndrome in patients mechanically ventilated > 48 hours in the ICU. Presentation at the SSCM Critical Care Congress, Orlando FL, Feb. 2007.

[3]

Lehman LH, Kyaw TH, Clifford GD, Mark RG. A temporal search engine for a massive multi-parameter clinical information database. Comput Cardiol, 34:637–640, Sept. 2007. (PDF)

[4]

Parlikar TA, Heldt T, Ranade GV, Verghese GC. Model-based estimation of cardiac output and total peripheral resistance. Comput Cardiol, 34:379–382, 2007. (PDF)

[5]

Villarroel M, Saeed A, Clifford GD, Moody GB, Mark RG. Finding relevant cases in large databases of signals, time series, and clinical data. Comput Cardiol, 34:265–268, Sept. 2007. (PDF)

[6]

Wolfberg AJ, Syed Z, Clifford GD, Tin A, Guttag J, du Plessis AJ. Entropy of fetal EKG associated with intrapartum fever. Presented at the New England Conference on Perinatal Research, Oct. 2007. (PDF)

Theses

[1]

Jia X. The effects of mechanical ventilation on the development of acute respiratory distress syndrome. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2007. (PDF)

[2]

Li SX. Probabilistic network models in cardiovascular monitoring. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[3]

Parlikar TA. Modeling and Monitoring of Cardiovascular Dynamics for Patients in Critical Care. Doctoral dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[4]

Saeed M. Temporal Pattern Recognition in Multiparameter ICU Data. Doctoral dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

[5]

Shavdia D. Septic shock: Providing early warnings using logistic regression models. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2007. (PDF)

[6]

Zamanian SA. Modeling and simulating human cardiovascular response to acceleration. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2007. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Clifford GD. A novel framework for signal representation and source separation. Journal of Biological Systems, 14(2):169–183, June 2006. (PDF)

[2]

He T, Clifford GD, Tarassenko L. Application of ICA in removing artefacts from the ECG. Neural Comput and Applic, 15(2):105–116, 2006. (PDF)

[3]

Heldt T. Continuous blood pressure-derived cardiac output monitoring — should we be thinking long term? J Appl Physiol, 101(2):373–374, 2006. Invited editorial. (PDF) (PMID:16690788)

[4]

Mukkamala R, Reisner TA, Hojman H, Mark RG, Cohen RJ. Continuous cardiac output monitoring by peripheral blood pressure waveform analysis. IEEE Trans Biomed Eng, 53(3):459–467, 2006. (PDF) (doi:10.1109/TBME.2005.869780) (PMID:16532772)

[5]

Parlikar TA, Heldt T, Verghese GC. Cycle-averaged models of cardiovascular dynamics. IEEE Transactions on Circuits and Systems–I: Fundamental Theory and Applications, 53(11):2459–2468, 2006. (PDF)

Conference proceedings and presentations

[1]

Clifford GD, Villarroel M. Model-based determination of QT intervals. Comput Cardiol, 33:357–360, 2006. (PDF)

[2]

Clifford GD, Aboukhalil A, Zong W, Sun JX, Janz BA, Moody GB, Mark RG. Using the blood pressure waveform to reduce critical false ECG alarms. Comput Cardiol, 33:829–832, 2006. (PDF)

[3]

Heldt T, Chernyak YB. Analytical solution to minimal cardiovascular model. Comput Cardiol, 33:785–788, Sept. 2006. (PDF)

[4]

Heldt T, Long W, Verghese GC, Szolovits P, Mark RG. Integrating data, models, and reasoning in critical care. Conf Proc IEEE Eng Med Biol Soc. 2006, 1:350–353, Sept. 2006. (PDF) (doi:10.1109/IEMBS.2006.259734) (PMID:17946818)

[5]

Moody GB, Koch H, Steinhoff U. The PhysioNet/Computers in Cardiology Challenge 2006: QT interval measurement. Comput Cardiol, 33:313–316, 2006. (PDF) (doi:10.1109/CIC.2004.1442881)

[6]

Roberts JM, Parlikar TA, Heldt T, Verghese GC. Bayesian networks for cardiovascular monitoring. Proceedings of the 28th IEEE Engineering in Medicine and Biology Conference, 205–209, 2006. (PDF) (PMID:17946804)

[7]

Saeed M, Mark RG. A novel method for the efficient retrieval of similar multiparameter physiologic time series using Wavelet-based symbolic representations. AMIA Annu Symp Proc, 679–683, 2006. (PDF) (PMID:17238427)

[8]

Sun JX, Reisner AT, Mark RG. A signal abnormality index for arterial blood pressure waveforms. Comput Cardiol, 33:13–16, Sept. 2006. (PDF)

[9]

Zong W, Saeed M, Heldt T. A QT interval detection algorithm based on ECG curve length transform. Comput Cardiol, 33:377–380, 2006. (PDF)

Books and book chapters

[1]

Clifford GD. Ch 3: ECG Statistics, Noise, Artifacts, and Missing Data in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 55–99.

[2]

Clifford GD. Ch 5: Linear Filtering Methods in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 135–170.

[3]

Clifford GD, Oefinger MB. Ch 2: ECG Acquisition, Storage, Transmission, and Representation in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 27–53.

[4]

Clifford GD, Azuaje F, McSharry PE, editors. Advanced Methods and Tools for ECG Analysis. 1st ed., Norwood, MA, USA: Artech House; 2006. (Engineering in Medicine and Biology; 1).

[5]

McSharry PE, Clifford GD. Ch 4: Models for ECG and RR interval Processes in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 101–133.

[6]

McSharry PE, Clifford GD. Ch 6: Nonlinear Filtering Methods in Advanced Methods and Tools for ECG Analysis, In: Clifford et al. [4]. 171–196.

[7]

Reisner AT, Clifford GD, Mark RG. Ch 1: The Physiological Basis of the Electrocardiogram in Advanced Methods and Tools for ECG Analysis. In Clifford GD, Azuaje F, McSharry PE, editors, Advanced Methods and Tools for ECG Analysis, number 1 in Engineering in Medicine and Biology, chapter 1, 1–25. Artech House, Norwood, MA, USA, 1st ed., Oct. 2006.

Theses

[1]

Hug C. Predicting the risk and trajectory of intensive care patients using survival models. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[2]

Neamatullah I. Automated de-identification of free text medical records. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[3]

Oefinger MB. Monitoring Transient Repolarization Segment Morphology Deviations in Mouse ECG. Doctoral dissertation, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

[4]

Roberts JM. Bayesian networks for cardiovascular monitoring. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2006. (PDF)

[5]

Sun JX. Cardiac output estimation using arterial blood pressure waveforms. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2006. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Clifford GD, Tarassenko L. Quantifying errors in spectral estimates of HRV due to beat replacement and resampling. IEEE Transactions in Biomedical Engineering, 52(4):630–638, 2005. (PDF) (doi:10.1109/TBME.2005.844028) (PMID:15825865)

[2]

Clifford GD, Shoeb A, McSharry PE, Janz BA. Model-based filtering, compression and classification of the ECG. International Journal of Bioelectromagnetism, 7(1):158–161, 2005. (PDF)

[3]

Heldt T, Chang JL, Chen JJS, Verghese GC, Mark RG. Cycle-averaged dynamics of a periodically driven, closed loop circulation model. Control Eng Pract, 13(9):1163–1171, Sept. 2005. (PDF) (PMID:16050064)

Conference proceedings and presentations

[1]

Clifford GD, McSharry PE. Method to filter ECGs and evaluate clinical parameter distortion using realistic ECG model parameter fitting. Comput Cardiol, 32:715–718, 2005. (PDF)

[2]

Clifford GD, Zapanta L, Janz BA, Mietus J, Mark RG. Segmentation of 24-hour cardiovascular activity using ECG-based sleep/sedation and noise metrics. Comput Cardiol, 32:595–598, 2005. (PDF)

[3]

Douglass M, Clifford GD, Reisner A, Long WJ, Moody GB, Mark RG. De-identification algorithm for free-text nursing notes. Comput Cardiol, 32:341–344, 2005. (PDF)

[4]

Heldt T, Mark RG. Understanding post-spaceflight orthostatic intolerance: a simulation study. Comput Cardiol, 32:631–634, 2005. (PDF)

[5]

Janz BA, Clifford GD, Mark RG. A multivariable analysis of sedation, activity and agitation in critically ill patients using the Riker scale, ECG, blood pressure and respiratory rate. Comput Cardiol, 32:735–738, 2005. (PDF)

[6]

Janz BA, Frassica J, Baker C, Clifford GD, Mark RG. A new paradigm for managing information in the ICU in response to the 80 hour work week. New England Surgical Society, 86:118–119, 2005. (PDF)

[7]

Janz BA, Saeed M, Frassica J, Clifford GD, Mark RG. Development and optimization of a Critical Care Alert and Display (CCAD) system using retrospective ICU databases. In AMIA Annu Symp Proc, volume 994, 2005. (PMID:16779281)

[8]

Janz BA, Saeed M, Frassica J, Clifford GD, Mark RG. Development and optimization of a critical care alert and display (CCAD) system using retrospective ICU databases. AMIA Annu Symp Proc, 2005. (PDF) (PMID:16779281)

[9]

McSharry PE, Clifford GD. A statistical model of the sleep-wake dynamics of the cardiac rhythm. Comput Cardiol, 32:591–594, 2005. (PDF)

[10]

Oefinger MB, Mark RG. A web-based tool for visualization and collaborative annotation of physiological databases. Comput Cardiol, 32, 2005. (PDF)

[11]

Oefinger MB, Krieger M, Mark RG. Long-term ECG trends in atherosclerotic mouse subjects. Comput Cardiol, 32:695–698, 2005. (PDF)

[12]

Parlikar TA, Verghese GC. A simple cycle-averaged model for cardiovascular dynamics. Proceedings of the 27th Annual IEEE Engineering in Medicine and Biology Society Conference, 27:5490–5494, 2005. (PDF) (PMID:17281496)

[13]

Saeed M, Janz B, Clifford GD, Abdala O, Kyaw T, Douglass M, Shu J, Reisner A, Long W, Szolovits P, Heldt T, Verghese G, Moody G, Mark. R. MIMIC II: A massive temporal database to support research in integrating data, models, and reasoning in critical care. AMIA conference, Oct. 2005, Washington DC., 2005.

[14]

Samar Z, Heldt T, Verghese GC, Mark RG. Model-based cardiovascular parameter estimation in the intensive care unit. Comput Cardiol, 32:635–638, 2005. (PDF)

[15]

Sun JX, Reisner AT, Saeed M, Mark RG. Estimating cardiac output from arterial blood pressure waveforms: a critical evaluation using the MIMIC II database. Comput Cardiol, 32:295–298, Sept. 2005. (PDF)

Theses

[1]

Abdala OT. The Annotation Station : an open source technology for data visualization and annotation of large biomedical databases. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[2]

Douglass M. Computer-assisted de-identification of free-text nursing notes. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Feb. 2005. (PDF)

[3]

Kyaw TH. Formatting and searching a massive, multi-parameter clinical information database. Master's thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[4]

Samar Z. Cardiovascular parameter estimation using a computational model. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, June 2005. (PDF)

[5]

Shu J. Free text phrase encoding and information extraction from medical notes. M.Eng. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

[6]

Zapanta LF. Heart rate variability in mice with coronary heart disease. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2005. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Clifford GD, Tarassenko L. Segmenting cardiac-related data using sleep stages increases separation between normal subjects and apnoeic patients. IOP Physiol Meas, (25):N27–N35, 2004. (PDF) (doi:10.1088/0967-3334/25/6/N03) (PMID:15712732)

[2]

Jager F, Moody GB, Mark RG. Protocol to assess robustness of ST analysers: a case study. Physiological Measurement, 25(3):629–643, 2004. (PDF) (doi:10.1088/0967-3334/25/3/004) (PMID:15253115)

[3]

Zong W, Moody GB, Mark RG. Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure. Med Biol Eng Comput, 42(5):698–706, Sept. 2004. (PDF) (PMID:15503972)

Conference proceedings and presentations

[1]

Abdala OT, Saeed M. Estimation of missing values in clinical laboratory measurements of ICU patients using a weighted K-nearest neighbors algorithm. Comput Cardiol, 31:693–696, 2004. (PDF)

[2]

Abdala OT, Clifford GD, Saeed M, Reisner A, Moody GB, Henry I, Mark RG. The Annotation Station: an open-source technology for annotating large biomedical databases. Comput Cardiol, 31:681–685, 2004. (PDF)

[3]

Ali W, Eshelman L, Saeed M. Identifying artifacts in arterial blood pressure using morphogram variability. Comput Cardiol, 31:697–700, Sept. 2004. (PDF)

[4]

Clifford GD, McSharry PE. Generating 24-hour ECG, BP and respiratory signals with realistic linear and nonlinear clinical characteristics using a nonlinear model. Comput Cardiol, 31:709–712, 2004. (PDF)

[5]

Clifford GD, McSharry PE. A nonlinear artificial model for generating realistic correlated ECG, BP and respiration. 17th international EURASIP conference, 358–360, June 2004. Biosignal2004, Brno, Czech Republic. (PDF)

[6]

Clifford GD, McSharry PE. A realistic coupled nonlinear artificial ECG, BP, and respiratory signal generator for assessing noise performance of biomedical signal processing algorithms. Proc of SPIE International Symposium on Fluctuations and Noise, 5467(34):290–301, 2004. (PDF)

[7]

Douglass M, Clifford GD, Reisner A, Moody GB, Mark RG. Computer-assisted de-identification of free text in the MIMIC II database. Comput Cardiol, 31:341–344, 2004. (PDF)

[8]

Healey J, Clifford GD, Kontothanassis L, McSharry PE. An open-source method for simulating atrial fibrillation using ECGSYN. Comput Cardiol, 31:425–427, 2004. (PDF)

[9]

Heldt T, Mark RG. Scaling cardiovascular parameters for population simulations. Comput Cardiol, 31:133–136, 2004. (PDF)

[10]

Jager F, Smrdel A, Mark RG. An open-source tool to evaluate performance of transient ST segment episode detection algorithms. Comput Cardiol, 31:585–588, 2004. (PDF)

[11]

McSharry PE, Clifford GD. A comparison of nonlinear noise reduction and independent component analysis using a realistic dynamical model of the electrocardiogram. Proc of SPIE International Symposium on Fluctuations and Noise, 5467(09):78–88, 2004. (PDF)

[12]

McSharry PE, Clifford GD. Open-source software for generating electrocardiogram signals. ARXIV preprints, 0406017, 2004. (PDF)

[13]

Moody GB. Spontaneous termination of atrial fibrillation: a challenge from PhysioNet and Computers in Cardiology 2004. Comput Cardiol, 31:101–104, Sept. 2004. (PDF) (doi:10.1109/CIC.2004.1442881)

[14]

Nam DS, Youn CH, Lee BH, Clifford GD, Healey J. QoS-constrained resource allocation for a Grid-based multiple source electrocardiogram application. Lecture Notes in Computer Science, 3043:352–359, 2004. Information Systems and Information Technologies (ISIT) Workshop, (Grid Session). (PDF)

[15]

Oefinger M, Moody GB, Krieger M, Mark RG. System for remote multi-channel real-time monitoring of ECG via the internet. Comput Cardiol, 31:753–756, 2004. (PDF)

[16]

Oefinger M, Zong W, Krieger M, Mark RG. An interactive web-based tool for multi-scale physiological data visualization. Comput Cardiol, 31:569–572, 2004. (PDF)

[17]

Shu J, Clifford GD, Saeed M, Long WJ, Moody GB, Szolovits P, Mark RG. An open-source, interactive Java-based system for rapid encoding of significant events in the ICU using the Unified Medical Language System. Comput Cardiol, 31:197–200, 2004. (PDF)

[18]

Wang H, Azuaje F, Clifford GD, Jung B, Black N. Methods and tools for generating and managing ecgML-based information. Comput Cardiol, 31:573–576, 2004. (PDF)

[19]

Youn CH, Kim B, Nam DS, Shim EB, Clifford GD, Healey J. Resource reconfiguration scheme based on temporal quorum status estimation in computational grids. Lecture Notes in Computer Science, 699–707, 2004. (PDF)

[20]

Youn CH, Nam DS, Kim B, An ES, Lee BH, Shim EB, Clifford GD. QoS quorum-constrained resource management in wireless grid. Lecture Notes in Computer Science, 3222:65–72, 2004. Network and Parallel Computing, (NPC 2004), IFIP International Conference, Wuhan, China, oct 18–20. (PDF)

Theses

[1]

Heldt T. Computational Models of Cardiovascular Function During Orthostatic Stress. Doctoral dissertation, Harvard–MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2004. (PDF)

[2]

Thorn K. Characterization of intravenous medication administration in an intensive care unit. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2004. (PDF)

Journal articles | Conference presentations | Books and book chapters | Theses

Journal articles

[1]

Costa M, Moody GB, Henry I, Goldberger AL. Physionet: an NIH research resource for complex signals. J Electrocardiology, 36(suppl):139–144, 2003. (PDF)

[2]

Jager F, Taddei A, Moody GB, Emdin M, Antolic G, Dorn R, Smrdel A, Marchesi C, Mark RG. Long-term ST database: a reference for the development and evaluation of automated ischaemia detectors and for the study of the dynamics of myocardial ischaemia. Med Biol Eng Comput, 41(2):172–182, Mar. 2003. (PMID:12691437)

[3]

McSharry PE, Clifford GD, Tarassenko L. A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans Biomed Eng, 50(3):289–294, 2003. (PDF) (doi:10.1109/TBME.2003.808805) (PMID:12669985)

Conference proceedings and presentations

[1]

Chen JJS, Heldt T, Verghese GC, Mark RG. Analytical solution to simplified circulatory model using piecewise linear elastance. Comput Cardiol, 30:45–48, 2003. (PDF)

[2]

Heldt T, Chang JL, Verghese GC, Mark RG. Cycle-averaged models of cardiovascular dynamics. Modelling and Control in Biomedical Systems 2003, 387–392, 2003. (PDF)

[3]

Heldt T, Oefinger MB, Hoshiyama M, Mark RG. Circulatory response to passive and active changes in posture. Comput Cardiol, 30:263–266, Sept. 2003. (PDF)

[4]

Heldt T, Verghese GC, Kamm RD, Mark RG. Modeling cardiovascular response to gravitational stress–combined forward and inverse approach. In IFMBE Proceedings — World Congress on Medical Physics and Biomedical Engineering, 2003.

[5]

Moody GB, Jager F. Distinguishing ischemic from non-ischemic ST changes: the PhysioNet/Computers in Cardiology Challenge 2003. Comput Cardiol, 30:235–237, 2003. (PDF) (doi:10.1109/CIC.2003.1291134)

[6]

Moody GB, Dakin M, Mark RG. Web-enabled physiologic signal processing and analysis. Proc. World Congress on Medical Physics and Biomedical Engineering, 2003. (PDF)

[7]

Mukkamala R, Reisner AT, Hojman HM, Mark RG, Cohen RJ. Continuous cardiac output monitoring by peripheral blood pressure waveform analysis. Comput Cardiol, 30:255–258, 2003. (PDF)

[8]

Zong W, Heldt T, Moody GB, Mark RG. An open-source algorithm to detect onset of arterial blood pressure pulses. Comput Cardiol, 30:259–262, 2003. (PDF)

[9]

Zong W, Moody GB, Jiang D. A robust open-source algorithm to detect onset and duration of QRS complexes. Comput Cardiol, 30:737–740, 2003. (PDF)

Theses

[1]

Chen JJS. Analytical solution to a simplified circulatory model using piecewise linear elastance function. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, July 2003.

[2]

Oefinger MB. System for remote multichannel real-time monitoring of mouse ECG via the Internet. M.S. Thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, Sept. 2003.

[1]

Mitchell WG, Pande R, Robinson TE, Jones GD, Hou I, , Celi LA. The weekend effect for stroke patients admitted to intensive care: A retrospective cohort analysis. PLoS One, 15(6):e0234521, June PLoS One. (doi:10.1371/journal.pone.0234521) (PMID:32520977)