Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Journal of Medical Internet Research2020Vol. 22(11), pp. e24018–e24018
Citations Over TimeTop 1% of 2020 papers
Akhil Vaid, Sulaiman Somani, Adam Russak, Jessica K. De Freitas, Fayzan Chaudhry, Ishan Paranjpe, Kipp W. Johnson, Samuel Lee, Riccardo Miotto, Felix Richter, Shan Zhao, Noam D. Beckmann, Nidhi Naik, Arash Kia, Prem Timsina, Anuradha Lala, Manish Paranjpe, Eddye Golden, Matteo Danieletto, Manbir Singh, Dara Meyer, Paul F. O’Reilly, Laura M. Huckins, Patricia Kovatch, Joseph Finkelstein, Robert Freeman, Edgar Argulian, Andrew Kasarskis, Bethany Percha, Judith A. Aberg, Emilia Bagiella, Carol R. Horowitz, Barbara Murphy, Eric J. Nestler, Eric E. Schadt, Judy H. Cho, Carlos Cordon‐Cardo, Valentı́n Fuster, Dennis S. Charney, David L. Reich, Erwin P. Böttinger, Matthew A. Levin, Jagat Narula, Zahi A. Fayad, Allan C. Just, Alexander W. Charney, Girish N. Nadkarni, Benjamin S. Glicksberg
Abstract
We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.
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