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April 3, 2026Geriatrics and gerontology international/Geriatrics & gerontology international6 citations

Machine Learning Approaches to Identify Physical, Psychological, and Social Predictors of Worsening Frailty Transitions Among Community‐Dwelling Older Adults: A Longitudinal Cohort Study

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ICI‐Hui ChenTYTzu‐Pei YehYTYi‐Hua Tang

Key Points

  • The study aims to identify physical, psychological, and social predictors of worsening frailty transitions among older adults using machine learning techniques.
  • Longitudinal cohort design
  • Analysis of multidimensional data
  • Application of machine learning approaches
  • Identification of specific predictors related to frailty transitions
  • Enhanced understanding of frailty progression
  • Potential support for developing targeted interventions

Abstract

Worsening frailty transitions result from multifactorial and complex interactions. Machine learning methods can effectively handle multidimensional data and reveal patterns not captured by traditional statistical approaches. These findings enhance our understanding of frailty progression and support the development of targeted interventions for older adults.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69cf5e995a333a821460d188https://doi.org/10.1111/ggi.70467
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Also Consider

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  1. 1Comment on “Machine Learning Approaches to Identify Physical, Psychological, and Social Predictors of Worsening Frailty Transitions Among Community‐Dwelling Older Adults: A Longitudinal Cohort Study”2026
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  4. 4Development and validation of machine learning models to predict frailty risk for elderly2024 · 6 citations
  5. 5Machine Learning-Based Frailty Prediction and Classification in Community-Dwelling Older Adults: A Systematic Review of Validation, Explainability, and Implementation Readiness2026