Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence - Takeaways - MDSpire

Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence

  • By

  • Ying Gao

  • Doudou Xu

  • Xinru Li

  • Jue Wang

  • Linbin Wang

  • Beiwen Wu

  • Haifeng Zhao

  • Xian Qiu

  • Weiyi Zhu

  • May 14, 2026

  • 0 min

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  • 1

    Falls are a significant health concern for community-dwelling older adults, with over 25% experiencing falls annually in the U.S.

  • 2

    Traditional fall risk assessment tools may not fully capture the complex factors contributing to falls in older adults living independently.

  • 3

    Machine learning and deep learning methods can enhance fall risk prediction by integrating diverse predictors and accommodating nonlinear relationships.

  • 4

    Existing reviews have largely overlooked ML-based models for predicting future falls in community-dwelling older adults, focusing instead on real-time detection.

  • 5

    This systematic review aims to evaluate the predictive performance and methodological quality of ML and DL models for fall risk in older adults.

Original Source(s)

Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence

  • by Ying Gao, Doudou Xu, Xinru Li, Jue Wang, Linbin Wang, Beiwen Wu, Haifeng Zhao, Xian Qiu, Weiyi Zhu

  • May 14, 2026

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