Identification and Comparison of Patient Characteristics for Those Hospitalized with COVID-19 versus Influenza Using Machine Learning in a Commercially Insured US Population.

Background The novel severe acute respiratory syndrome coronavirus 2, the virus that causes coronavirus disease 2019 (COVID–19), continues to spread in the US through the 2020–2021 influenza season and beyond. Approaches to identify those most at risk for poor outcomes for the two viral infections are needed for future planning. As influenza is a well-known respiratory disease sharing some similarities to COVID-19, such comparison will aid physicians and health systems to predict disease trajectory and allocate health resources most efficiently. A retrospective cohort study using a French national administrative database found that patients hospitalized with COVID-19 were more frequently obese or overweight, diabetic, and hypertensive. 1 Patients hospitalized with influenza more frequently had heart failure, chronic respiratory disease, and cirrhosis. 1 Similar observations were reported in an international network study that included US, South Korea, and Spain. 2 While this information provides useful context to the current understanding of characteristics of patients hospitalized with COVID-19 in several countries, understanding of the overall risk profile for the two viral infections is lacking in a broad US population. Advanced modelling, machine learning, and artificial intelligence (AI) techniques have been employed to detect, diagnose, evaluate, and prioritize for Examples include laboratory examination frameworks to prioritize patients with COVID-19, AI techniques in the detection and classification of COVID-19 medical images, and models to predict the spread of disease. An increasing number of severe COVID-19 outcome risk assessment studies found that demographic factors, comorbidities, radiographic findings, and laboratory markers may individually or collectively predict worse outcomes. 3 deepen the understanding of COVID-19,