Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City

Supervised Machine Learning Approach to Identify Early Predictors of Poor Outcome in Patients with COVID-19 Presenting to a Large Quaternary Care Hospital in New York City

Background: The progression of clinical manifestations in patients with coronavirus disease 2019 (COVID-19) highlights the need to account for symptom duration at the time of hospital presentation in decision-making algorithms. Methods: We performed a nested case–control analysis of 4103 adult patie...

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Bibliographic Details
Main Authors: Jason Zucker, Angela Gomez-Simmonds, Lawrence J. Purpura, Sherif Shoucri, Elijah LaSota, Nicholas E. Morley, Brit W. Sovic, Marvin A. Castellon, Deborah A. Theodore, Logan L. Bartram, Benjamin A. Miko, Matthew L. Scherer, Kathrine A. Meyers, William C. Turner, Maureen Kelly, Martina Pavlicova, Cale N. Basaraba, Matthew R. Baldwin, Daniel Brodie, Kristin M. Burkart, Joan Bathon, Anne-Catrin Uhlemann, Michael T. Yin, Delivette Castor, Magdalena E. Sobieszczyk
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Journal of Clinical Medicine
Subjects:
COVID
outcomes
machine learning
Online Access:https://www.mdpi.com/2077-0383/10/16/3523

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