Open AccessA Machine Learning Algorithm Predicts Duration of hospitalization in COVID-19 patients
Author(s) -
Susan Cheng,
Michael Wells,
David Ouyang,
Tod Davis,
Noy Kaufman,
Susan Cheng,
Sumeet S. Chugh
Publication year - 2021
Publication title -
intelligence-based medicine
Language(s) - English
Resource type - Journals
ISSN - 2666-5212
DOI - 10.1016/j.ibmed.2021.100035
Subject(s) - covid-19 , medicine , cohort , machine learning , electronic medical record , pandemic , medical record , healthcare system , artificial intelligence , duration (music) , electronic health record , emergency medicine , maximum likelihood , health care , medical emergency , computer science , statistics , mathematics , disease , infectious disease (medical specialty) , economics , economic growth , art , literature
The COVID-19 pandemic has placed unprecedented strain on the healthcare system, particularly hospital bed capacity in the setting of large variations in patient length of stay (LOS). Using electronic health record data from 966 COVID-19 patients at a large academic medical center, we developed three machine learning algorithms to predict the likelihood of prolonged LOS, defined as >8 days. The models included 353 variables and were trained on 80% of the cohort, with 20% used for model validation. The three models were created on hospital days 1, 2 and 3, each including information available at or before that point in time. The models’ predictive capabilities improved sequentially over time, reaching an accuracy of 0.765, with an AUC of 0.819 by day 3. These models, developed using readily available data, may help hospital systems prepare for bed capacity needs, and help clinicians counsel patients on their likelihood of prolonged hospitalization.