This paper is in the following e-collection/theme issue:
JMIR Theme Issue: COVID-19 Special Issue (2548) Decision Support for Health Professionals (1515) Electronic Health Records (1297) Machine Learning (2047) Theme Issue: Medical Informatics and COVID-19 (124) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1541)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, 3
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