| 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 | 466 | 2016 |
| 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), 2018 | 298 | 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 | 253 | 2016 |
| 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 | 148 | 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 | 143 | 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 | 128 | 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 | 123 | 2017 |
| G-quadruplex formation in double strand DNA probed by NMM and CV fluorescence A Kreig, J Calvert, J Sanoica, E Cullum, R Tipanna, S Myong Nucleic acids research 43 (16), 7961-7970, 2015 | 87 | 2015 |
| 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 | 78 | 2019 |
| Telomeric overhang length determines structural dynamics and accessibility to telomerase and ALT-associated proteins H Hwang, A Kreig, J Calvert, J Lormand, Y Kwon, JM Daley, P Sung, ... Structure 22 (6), 842-853, 2014 | 77 | 2014 |
| 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 | 75 | 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 | 60 | 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 |
| Brownian structure in the KPZ fixed point J Calvert, A Hammond, M Hegde Astérisque 441, 2023 | 45* | 2023 |
| 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 | 42 | 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 |
| Convolutional neural network model for intensive care unit acute kidney injury prediction S Le, A Allen, J Calvert, PM Palevsky, G Braden, S Patel, E Pellegrini, ... Kidney international reports 6 (5), 1289-1298, 2021 | 33 | 2021 |
| Cost and mortality impact of an algorithm-driven sepsis prediction system J Calvert, J Hoffman, C Barton, D Shimabukuro, M Ries, U Chettipally, ... Journal of medical economics 20 (6), 646-651, 2017 | 30 | 2017 |
| A machine learning approach to predict deep venous thrombosis among hospitalized patients L Ryan, S Mataraso, A Siefkas, E Pellegrini, G Barnes, A Green-Saxena, ... Clinical and Applied Thrombosis/Hemostasis 27, 1076029621991185, 2021 | 29 | 2021 |
Ritankar DasVerified email at fortahealth.com
