Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.

Saved in:
Bibliographic Details
Title: Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.
Authors: Khani M; Department of Health Informatics & Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA., Friedland DR; Keck School of Medicine, University of Southern California, Los Angeles, CA, USA., Widlansky M; Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, USA., Harris MS; Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, USA., Adams J; Department of Otolaryngology & Communication Sciences, Medical College of Wisconsin, Milwaukee, WI, USA., Fan L; Department of Biomedical Engineering, Marquette University, Milwaukee, WI, USA., Oh H; School of Nursing, University of Wisconsin-Milwaukee, Milwaukee, WI, USA., Lu Q; Department of Computer Science & Technology, University of Petroleum, Beijing, China., Luo J; Department of Health Informatics & Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA. jakeluo@uwm.edu.
Source: GeroScience [Geroscience] 2026 Feb 04. Date of Electronic Publication: 2026 Feb 04.
Publication Type: Journal Article
Journal Info: Publisher: Springer International Publishing Country of Publication: Switzerland NLM ID: 101686284 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2509-2723 (Electronic) Linking ISSN: 25092723 NLM ISO Abbreviation: Geroscience Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2509-2723
DOI:10.1007/s11357-026-02117-x