The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: evidence from Wuhan-area hospitals
Research finds machine learning is reasonable in predicting LOS before and during the COVID-19 pandemic. This study offers valuable guidance to hospital administrators for planning resource allocation strategies that can effectively meet the demand. Consequently, these insights contribute to improved quality of care and wiser utilization of scarce resources.
Authors
- Chengzhi Zhang
- Yang Liu (ORCID: https://orcid.org/0000-0002-0166-3944)
- Ruonan Liang
Institutions
- Nanjing University of Science and Technology (CN)
- Wuhan University (CN)
- City University of Hong Kong, Shenzhen Research Institute (CN)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2024-12-13
- DOI
- https://doi.org/10.3389/fdgth.2024.1506071
- Citations
- 1
- Primary Topic
- COVID-19 and healthcare impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.28
Funders
- National Natural Science Foundation of China
- Ministry of Education of the People's Republic of China
- China Postdoctoral Science Foundation
- Fundamental Research Funds for the Central Universities
- Basic and Applied Basic Research Foundation of Guangdong Province