MIMIC-III, a freely accessible critical care database AEW Johnson, TJ Pollard, L Shen, LH Lehman, M Feng, M Ghassemi, ... Scientific data 3 (1), 1-9, 2016 | 7862 | 2016 |
The eICU Collaborative Research Database, a freely available multi-center database for critical care research TJ Pollard, AEW Johnson, JD Raffa, LA Celi, RG Mark, O Badawi Scientific data 5 (1), 1-13, 2018 | 1317 | 2018 |
The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care M Komorowski, LA Celi, O Badawi, AC Gordon, AA Faisal Nature medicine 24 (11), 1716-1720, 2018 | 1100 | 2018 |
Mimic-iv A Johnson, L Bulgarelli, T Pollard, S Horng, LA Celi, R Mark PhysioNet. Available online at: https://physionet. org/content/mimiciv/1.0 …, 2020 | 970 | 2020 |
MIMIC-IV, a freely accessible electronic health record dataset AEW Johnson, L Bulgarelli, L Shen, A Gayles, A Shammout, S Horng, ... Scientific data 10 (1), 1, 2023 | 857 | 2023 |
The murine CAR homolog is a receptor for coxsackie B viruses and adenoviruses JM Bergelson, A Krithivas, L Celi, G Droguett, MS Horwitz, T Wickham, ... Journal of virology 72 (1), 415-419, 1998 | 496 | 1998 |
Mechanical power of ventilation is associated with mortality in critically ill patients: an analysis of patients in two observational cohorts A Serpa Neto, RO Deliberato, AEW Johnson, LD Bos, P Amorim, ... Intensive care medicine 44, 1914-1922, 2018 | 476 | 2018 |
Guidelines for reinforcement learning in healthcare O Gottesman, F Johansson, M Komorowski, A Faisal, D Sontag, ... Nature medicine 25 (1), 16-18, 2019 | 474 | 2019 |
The MIMIC Code Repository: enabling reproducibility in critical care research AEW Johnson, DJ Stone, LA Celi, TJ Pollard Journal of the American Medical Informatics Association 25 (1), 32-39, 2018 | 421 | 2018 |
Predicting in-hospital mortality of icu patients: The physionet/computing in cardiology challenge 2012 I Silva, G Moody, DJ Scott, LA Celi, RG Mark 2012 computing in cardiology, 245-248, 2012 | 408 | 2012 |
The “inconvenient truth” about AI in healthcare T Panch, H Mattie, LA Celi NPJ digital medicine 2 (1), 1-3, 2019 | 405 | 2019 |
AI recognition of patient race in medical imaging: a modelling study JW Gichoya, I Banerjee, AR Bhimireddy, JL Burns, LA Celi, LC Chen, ... The Lancet Digital Health 4 (6), e406-e414, 2022 | 376 | 2022 |
The myth of generalisability in clinical research and machine learning in health care J Futoma, M Simons, T Panch, F Doshi-Velez, LA Celi The Lancet Digital Health 2 (9), e489-e492, 2020 | 335 | 2020 |
ICU admission characteristics and mortality rates among elderly and very elderly patients L Fuchs, CE Chronaki, S Park, V Novack, Y Baumfeld, D Scott, ... Intensive care medicine 38, 1654-1661, 2012 | 334 | 2012 |
Early intervention with erythropoietin does not affect the outcome of acute kidney injury (the EARLYARF trial) ZH Endre, RJ Walker, JW Pickering, GM Shaw, CM Frampton, ... Kidney international 77 (11), 1020-1030, 2010 | 292 | 2010 |
The association between the neutrophil-to-lymphocyte ratio and mortality in critical illness: an observational cohort study JD Salciccioli, DC Marshall, MAF Pimentel, MD Santos, T Pollard, LA Celi, ... Critical care 19, 1-8, 2015 | 263 | 2015 |
Big data in global health: improving health in low-and middle-income countries R Wyber, S Vaillancourt, W Perry, P Mannava, T Folaranmi, LA Celi Bulletin of the World Health Organization 93, 203-208, 2015 | 250 | 2015 |
Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives S Gehrmann, F Dernoncourt, Y Li, ET Carlson, JT Wu, J Welt, J Foote Jr, ... PloS one 13 (2), e0192360, 2018 | 248 | 2018 |
Continuous state-space models for optimal sepsis treatment: a deep reinforcement learning approach A Raghu, M Komorowski, LA Celi, P Szolovits, M Ghassemi Machine Learning for Healthcare Conference, 147-163, 2017 | 248 | 2017 |
Deep reinforcement learning for sepsis treatment A Raghu, M Komorowski, I Ahmed, L Celi, P Szolovits, M Ghassemi arXiv preprint arXiv:1711.09602, 2017 | 232 | 2017 |