This paper is in the following e-collection/theme issue:

Machine Learning (1937) Decision Support for Health Professionals (1453) Frailty Detection, Assessment and Prediction (50) AI in Older Adult Care (36) Applications of AI (235) mHealth for Diagnosis (76) Artificial Intelligence (1971)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57298, first published .
Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study

Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study

Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study

Authors of this article:

Taehwan Kim1 Author Orcid Image ;   Jung-Yeon Choi2 Author Orcid Image ;   Myung Jin Ko1 Author Orcid Image ;   Kwang-il Kim2, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Lai J, Cheng C, Liang T, Tang L, Guo X, Liu X. Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction. Journal of Translational Medicine 2025;23(1) View
  2. Perchoux C, Topalian N, Klein S, Chaix B, Tharrey M, Röcke C, Gerber P, Klein O, Missling A, Omrani H, Helbich M, Van Dyck D, Kestens Y, Dijst M, Fagherazzi G. Geographic environments, daily activities and stress in Luxembourg (the FragMent study): a protocol combining map-based questionnaires, geographically explicit ecological momentary assessment and vocal biomarkers of stress. BMJ Open 2025;15(9):e105499 View