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

BackgroundCOVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet th...

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Main Authors: Vaid, Akhil, Somani, Sulaiman, Russak, Adam J, De Freitas, Jessica K, Chaudhry, Fayzan F, Paranjpe, Ishan, Johnson, Kipp W, Lee, Samuel J, Miotto, Riccardo, Richter, Felix, Zhao, Shan, Beckmann, Noam D, Naik, Nidhi, Kia, Arash, Timsina, Prem, Lala, Anuradha, Paranjpe, Manish, Golden, Eddye, Danieletto, Matteo, Singh, Manbir, Meyer, Dara, O'Reilly, Paul F, Huckins, Laura, Kovatch, Patricia, Finkelstein, Joseph, Freeman, Robert M., Argulian, Edgar, Kasarskis, Andrew, Percha, Bethany, Aberg, Judith A, Bagiella, Emilia, Horowitz, Carol R, Murphy, Barbara, Nestler, Eric J, Schadt, Eric E, Cho, Judy H, Cordon-Cardo, Carlos, Fuster, Valentin, Charney, Dennis S, Reich, David L, Bottinger, Erwin P, Levin, Matthew A, Narula, Jagat, Fayad, Zahi A, Just, Allan C, Charney, Alexander W, Nadkarni, Girish N, Glicksberg, Benjamin S
Format: Article
Language:English
Published: JMIR Publications 2020-11-01
Series:Journal of Medical Internet Research
Online Access:https://www.jmir.org/2020/11/e24018

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