The COVID-19 pandemic has exacerbated existing hospital capacity limitations in the United States, causing hospitals in certain regions to hit maximum capacity. The purpose of this study is to investigate key features of COVID-19 related admissions to help create a higher level of public understanding and help guide healthcare management professionals and governments when considering preventive measures. The introduction of preventative measures and new regulations during the pandemic have led to the generation of multiple types of models and feature selection methods in the field of Machine Learning that are increasingly complicated. This study focuses on the exploration of feature selection through building multiple models, one simple linear model and one decision tree model for prediction on inpatient hospitalization rates. This will result in a highly interpretable model that can be more readily understood and easily used.
Barrera, Helene; Ehly, Justin; Freeman, Blake; Papesh, Chris; and Blanchard, Brad
"Using Hospital Bed Capacity Prediction During COVID-19 to Determine Feature Importance,"
SMU Data Science Review: Vol. 6:
1, Article 7.
Available at: https://scholar.smu.edu/datasciencereview/vol6/iss1/7
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