The Identification of Risk Factors Associated with COVID-19 in a Large Inpatient Cohort Using Machine Learning Approaches
Document Type
Article
Abstract
Coronavirus disease 2019 (COVID-19) has become a global pandemic that significantly challenged healthcare systems worldwide, with over 4 million deaths among 18.6 million identified cases as of June 2021. Understanding the current COVID-19 cases and determining clinical solutions is of paramount importance. In this chapter, we describe an exploratory study of identifying risk factors associated with COVID-19 inpatient care. Based on a set of COVID-19 inpatient medical health records in a US hospital system, we used both unsupervised and supervised machine learning methods to explore risk factors associated with hospitalized COVID-19 patients. We found that the most important features related to the COVID-19 disease include (1) influenza vaccines, (2) pneumococcal vaccines, and (3) weight-related variables (i.e., weight, height, and BMI). As such, we provide a use case that machine learning methods are valuable for predicting COVID-19 inpatient risk factors, and the results are promising to guide further research in this area.
Digital Object Identifier (DOI)
Publication Info
Published in Digital Innovation for Healthcare in Covid 19 Pandemic Strategies and Solutions, Volume 1, 2022, pages 189-199.
APA Citation
Wu, D., Ren, Y., He, L., & Johnson, J. (2022). The identification of risk factors associated with COVID-19 in a large inpatient cohort using machine learning approaches. Elsevier EBooks, 1, 189–199.https://doi.org/10.1016/B978-0-12-821318-6.00017-7
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