This paper is in the following e-collection/theme issue:
JMIR Theme Issue: COVID-19 Special Issue (2458) Decision Support for Health Professionals (1201) Electronic Health Records (1065) Machine Learning (1489) Theme Issue: Medical Informatics and COVID-19 (107) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520)Published on in Vol 22, No 11 (2020): November
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/24018, first published
.
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Authors of this article:
Akhil Vaid1 ; Sulaiman Somani1 ; Adam J Russak1, 2 ; Jessica K De Freitas1, 3 ; Fayzan F Chaudhry1, 3 ; Ishan Paranjpe1 ; Kipp W Johnson3 ; Samuel J Lee1 ; Riccardo Miotto1, 3 ; Felix Richter1, 3 ; Shan Zhao1, 4 ; Noam D Beckmann3 ; Nidhi Naik1 ; Arash Kia5, 6 ; Prem Timsina5, 6 ; Anuradha Lala5, 7 ; Manish Paranjpe8 ; Eddye Golden1 ; Matteo Danieletto1 ; Manbir Singh1 ; Dara Meyer3 ; Paul F O'Reilly3, 9, 10 ; Laura Huckins3, 9, 10 ; Patricia Kovatch11 ; Joseph Finkelstein5 ; Robert M. Freeman5, 6 ; Edgar Argulian12, 13 ; Andrew Kasarskis3, 5, 14, 15 ; Bethany Percha2 ; Judith A Aberg2, 16 ; Emilia Bagiella6, 7 ; Carol R Horowitz2, 5 ; Barbara Murphy2 ; Eric J Nestler17, 18 ; Eric E Schadt3, 14 ; Judy H Cho19 ; Carlos Cordon-Cardo20 ; Valentin Fuster7, 12, 13 ; Dennis S Charney21 ; David L Reich4 ; Erwin P Bottinger1, 22 ; Matthew A Levin3, 4 ; Jagat Narula12, 13 ; Zahi A Fayad23, 24 ; Allan C Just25 ; Alexander W Charney3, 9, 10 ; Girish N Nadkarni1, 2, 19 ; Benjamin S Glicksberg1, 3Altmetric
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