作者
Egbe-Etu Etu, Leslie Monplaisir, Suzan Arslanturk, Sara Masoud, Celestine Aguwa, Ihor Markevych, Joseph Miller
发表日期
2022/4/18
期刊
IEEE Access
卷号
10
页码范围
42243-42251
出版商
IEEE
简介
The coronavirus disease (COVID-19) outbreak has become a global public health threat. The influx of COVID-19 patients has prolonged the length of stay (LOS) in the emergency department (ED) in the United States. Our objective is to develop a reliable prediction model for COVID-19 patient ED LOS and identify clinical factors, such as age and comorbidities, associated with LOS within a “4-hour target.” Data were collected from an urban, demographically diverse hospital in Detroit for all COVID-19 patients’ ED presentations from March 16 to December 29, 2020. We trained four machine learning models, namely logistic regression (LR), gradient boosting (GB), decision tree (DT), and random forest (RF), across different data processing stages to predict COVID-19 patients with an ED LOS of less than or greater than 4 hours. The analysis is inclusive of 3,301 COVID-19 patients with known ED LOS, and 16 …
引用总数
20212022202320241186