Machine Learning–Driven COVID-19 Hospitalization Forecasting: From Theory to Practice in a Major Northeastern Academic Medical Center
The enhanced architecture significantly improves the forecasting of COVID-19 hospitalizations, particularly in anticipating peaks and resurgences. Its successful implementation in a hospital system highlights its potential for aiding decision-making and resource planning during pandemics and other respiratory disease outbreaks.
Authors
- Jeremiah S. Hinson (ORCID: https://orcid.org/0000-0002-2024-2360)
- Eili Klein (ORCID: https://orcid.org/0000-0002-1304-5289)
- Fardad Haghpanah (ORCID: https://orcid.org/0000-0002-2158-8486)
- Nodar Kipshidze (ORCID: https://orcid.org/0000-0003-1748-6679)
- Alexander Tulchinsky
- Xihan Zhao (ORCID: https://orcid.org/0000-0002-6834-7989)
Institutions
- Johns Hopkins University (US)
- Johns Hopkins Medicine (US)
- Health Trust (US)
- Education Trust (US)
Publication Details
- Journal
- Open Forum Infectious Diseases
- Published
- 2025-05-31
- DOI
- https://doi.org/10.1093/ofid/ofaf307
- Citations
- 1
- Primary Topic
- Forecasting Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.11
Funders
- National Institutes of Health
- Centers for Disease Control and Prevention