Document Type
Article
Publication Date
6-18-2022
Abstract
Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.
Recommended Citation
Y. Wen, M.F. Rahman, Y. Zhuang et al., Time-to-event modeling for hospital length of stay prediction for COVID-19 patients. Machine Learning with Applications 9: 100365 (2022), https://doi.org/10.1016/j.mlwa.2022.100365
Copyright
Elsevier
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Included in
Epidemiology Commons, Health Services Administration Commons, Health Services Research Commons, Other Public Health Commons
Comments
This article was originally published in Machine Learning with Applications, volume 9, in 2022. https://doi.org/10.1016/j.mlwa.2022.100365
This scholarship is part of the Chapman University COVID-19 Archives.