| Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach T Desautels, J Calvert, J Hoffman, M Jay, Y Kerem, L Shieh, ... JMIR medical informatics 4 (3), e5909, 2016 | 468 | 2016 |
| Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial DW Shimabukuro, CW Barton, MD Feldman, SJ Mataraso, R Das BMJ open respiratory research 4 (1), e000234, 2017 | 312 | 2017 |
| Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU Q Mao, M Jay, JL Hoffman, J Calvert, C Barton, D Shimabukuro, L Shieh, ... BMJ open 8 (1), e017833, 2018 | 297 | 2018 |
| A computational approach to early sepsis detection JS Calvert, DA Price, UK Chettipally, CW Barton, MD Feldman, ... Computers in biology and medicine 74, 69-73, 2016 | 255 | 2016 |
| Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor … A McCoy, R Das BMJ open quality 6 (2), e000158, 2017 | 151 | 2017 |
| Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial H Burdick, C Lam, S Mataraso, A Siefkas, G Braden, RP Dellinger, ... Computers in biology and medicine 124, 103949, 2020 | 150 | 2020 |
| Prediction of acute kidney injury with a machine learning algorithm using electronic health record data H Mohamadlou, A Lynn-Palevsky, C Barton, U Chettipally, L Shieh, ... Canadian journal of kidney health and disease 5, 2054358118776326, 2018 | 146 | 2018 |
| Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs C Barton, U Chettipally, Y Zhou, Z Jiang, A Lynn-Palevsky, S Le, J Calvert, ... Computers in biology and medicine 109, 79-84, 2019 | 132 | 2019 |
| Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach T Desautels, R Das, J Calvert, M Trivedi, C Summers, DJ Wales, A Ercole BMJ open 7 (9), e017199, 2017 | 122 | 2017 |
| Energy landscapes for machine learning AJ Ballard, R Das, S Martiniani, D Mehta, L Sagun, JD Stevenson, ... Physical Chemistry Chemical Physics 19 (20), 12585-12603, 2017 | 118 | 2017 |
| Using electronic health record collected clinical variables to predict medical intensive care unit mortality J Calvert, Q Mao, JL Hoffman, M Jay, T Desautels, H Mohamadlou, ... Annals of medicine and surgery 11, 52-57, 2016 | 84 | 2016 |
| Pediatric severe sepsis prediction using machine learning S Le, J Hoffman, C Barton, JC Fitzgerald, A Allen, E Pellegrini, J Calvert, ... Frontiers in pediatrics 7, 413, 2019 | 80 | 2019 |
| Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS) S Le, E Pellegrini, A Green-Saxena, C Summers, J Hoffman, J Calvert, ... Journal of Critical Care 60, 96-102, 2020 | 77 | 2020 |
| Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study L Ryan, C Lam, S Mataraso, A Allen, A Green-Saxena, E Pellegrini, ... Annals of Medicine and Surgery 59, 207-216, 2020 | 75 | 2020 |
| Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data … H Burdick, E Pino, D Gabel-Comeau, A McCoy, C Gu, J Roberts, S Le, ... BMJ health & care informatics 27 (1), 2020 | 62 | 2020 |
| Using transfer learning for improved mortality prediction in a data-scarce hospital setting T Desautels, J Calvert, J Hoffman, Q Mao, M Jay, G Fletcher, C Barton, ... Biomedical informatics insights 9, 1178222617712994, 2017 | 61 | 2017 |
| High-performance detection and early prediction of septic shock for alcohol-use disorder patients J Calvert, T Desautels, U Chettipally, C Barton, J Hoffman, M Jay, Q Mao, ... Annals of medicine and surgery 8, 50-55, 2016 | 59 | 2016 |
| Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus A Allen, Z Iqbal, A Green-Saxena, M Hurtado, J Hoffman, Q Mao, R Das BMJ Open Diabetes Research and Care 10 (1), e002560, 2022 | 47 | 2022 |
| A racially unbiased, machine learning approach to prediction of mortality: algorithm development study A Allen, S Mataraso, A Siefkas, H Burdick, G Braden, RP Dellinger, ... JMIR public health and surveillance 6 (4), e22400, 2020 | 44 | 2020 |
| Machine-learning-based laboratory developed test for the diagnosis of sepsis in high-risk patients J Calvert, N Saber, J Hoffman, R Das Diagnostics 9 (1), 20, 2019 | 37 | 2019 |
