Formulating and evaluating time series algorithms to forecast daily asthma hospital admissions
Our Ensemble model of mean predictions from ARIMA, ETS, and Prophet models was the most accurate in forecasting future asthma hospitalizations. Integrating forecasting techniques with clinical operations could enable proactive prevention through enhanced population care management.
Authors
- David Hartley (ORCID: https://orcid.org/0000-0003-2589-2538)
- Michael Seid (ORCID: https://orcid.org/0000-0001-9773-9263)
- Cole Brokamp (ORCID: https://orcid.org/0000-0002-0289-3151)
- Andrew F. Beck (ORCID: https://orcid.org/0000-0002-4262-663X)
- Joseph Bruce
- Mfonobong Udoko
- Stephen P. Colegate (ORCID: https://orcid.org/0000-0002-4943-8594)
- Joseph Michael
- Aaron Flicker
Institutions
- Cincinnati Children's Hospital Medical Center (US)
- Pulmonary Associates (US)
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- Journal of Clinical and Translational Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/cts.2025.10111
- Primary Topic
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cincinnati Children's Hospital Medical Center
- National Institutes of Health
- Agency for Healthcare Research and Quality
- U.S. National Library of Medicine
- National Center for Advancing Translational Sciences