biostudies-literatureUnknown9Yang X<h4>Background</h4>Falls are one of the leading causes of injury or death among older adults. Falls occurring in individuals during hospitalization, as an adverse event, are a key concern for health care institutions. Identifying older adults at high risk of falls in clinical settings enables early interventions, thereby reducing the incidence of falls.<h4>Objective</h4>This study aims to develop and validate machine learning models to predict the risk of falls among hospitalized older adults.<h4>Methods</h4>This study retrospectively analyzed data from a tertiary general hospital in China, including 342 older adults who experienced falls and 684 randomly matched nonfallers, between January 2018 and December 2024, encompassing demographic information, comorbidities, laboratory parameters, JMIR aginge80602https://www.ebi.ac.uk/biostudies/studies/S-EPMC12767673biostudies-literatureDevelopment and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study.PMC12767673Su DLi JZhang MWei ZZhang SXu JDai XYang XLi XBao MLi YMo NRen JZhang YChen XWang ZfalseDevelopment and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study.<h4>Background</h4>Falls are one of the leading causes of injury or death among older adults. Falls occurring in individuals during hospitalization, as an adverse event, are a key concern for health care institutions. Identifying older adults at high risk of falls in clinical settings enables early interventions, thereby reducing the incidence of falls.<h4>Objective</h4>This study aims to develop and validate machine learning models to predict the risk of falls among hospitalized older adults.<h4>Methods</h4>This study retrospectively analyzed data from a tertiary general hospital in China, including 342 older adults who experienced falls and 684 randomly matched nonfallers, between January 2018 and December 2024, encompassing demographic information, comorbidities, laboratory parameters, 2026-01-01T00:00:00Z2026 Jan2026-06-06T11:55:24.041Z2026-05-30T03:09:07.694ZS-EPMC127676734148998810.2196/80602