Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic Modeling Study
Using a nationally representative cohort and time-varying predictors, our model offers a practical approach for efficiently identifying older adults at high FRI risk, which can be updated over time. This approach can inform clinical decision-making and optimize the allocation of fall prevention resources.
Authors
- Grace Hsin‐Min Wang (ORCID: https://orcid.org/0000-0001-7855-9590)
- Amie J. Goodin (ORCID: https://orcid.org/0000-0002-0020-8720)
- Ronald I. Shorr (ORCID: https://orcid.org/0000-0002-1457-4668)
- Rachel Reise (ORCID: https://orcid.org/0000-0003-4410-5907)
- Y.-H. Lee (ORCID: https://orcid.org/0009-0008-3971-4242)
- Wei‐Hsuan Lo‐Ciganic (ORCID: https://orcid.org/0000-0001-6590-4770)
Institutions
- University of Pittsburgh (US)
- American Association of Colleges of Pharmacy (US)
- University of Florida Health (US)
- University of Florida (US)
- North Florida/South Georgia Veterans Health System (US)
- Global Policy Institute (US)
- Florida College (US)
Publication Details
- Journal
- Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy
- Published
- 2025-11-27
- DOI
- https://doi.org/10.1002/phar.70087
- Citations
- 1
- Primary Topic
- Balance, Gait, and Falls Prevention
- Type
- article
- Field-Weighted Citation Impact
- 4.76