ORIGINAL RESEARCH article
Front. Public Health
Sec. Aging and Public Health
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1614374
Multimodal Data-Driven Prognostic Model for Predicting Long-Term Outcomes in Elderly Patients With Sarcopenia: A Retrospective Cohort Study
Provisionally accepted
- Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Background： Sarcopenia (SP) is a progressive, age-related disease that may result in various adverse health outcomes and even mortality in older adults. Accurately predicting the mortality risk of older adults with SP is essential for informed clinical decision-making. This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk stratification and management in older adults with SP.
Keywords: Sarcopenia, machine learning, Multimodal data, Mortality prediction, nomogram
Received: 18 Apr 2025; Accepted: 16 Jul 2025.
Copyright: © 2025 刘, Wu, Guo and Peng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jinhui Wu, Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.