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Machine Learning Algorithms for Predicting Injurious Fall Risk Among Older Adults With Depression: A Prognostic

Grace Hsin-Min Wang1, Yao-An Lee1, Amie J Goodin1

  • 1Department of Pharmaceutical Outcomes & Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA.

Pharmacotherapy
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict 3-month fall and related injury (FRI) risk in older adults with depression. This approach identifies high-risk individuals for timely interventions, improving care and resource allocation.

Keywords:
depressionelderlyfallsmachine learning

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Area of Science:

  • Gerontology
  • Data Science in Healthcare
  • Public Health

Background:

  • Falls and related injuries (FRI) represent a significant health burden for older adults experiencing depression.
  • Current prediction models lack the ability to adapt to dynamic health changes over time, limiting proactive intervention.

Purpose of the Study:

  • To develop and validate machine learning algorithms for predicting 3-month FRI risk in older adults with depression.
  • To compare the performance of elastic net, random forest, and gradient boosting machine models.

Main Methods:

  • Utilized a national cohort of fee-for-service Medicare beneficiaries aged 65+ with depression diagnoses.
  • Employed 261 time-varying predictors updated every 3 months to forecast subsequent 3-month FRI risk.
  • Assessed model performance using c-statistics and risk stratification.

Main Results:

  • The random forest model achieved a c-statistic of 0.68, capturing 68.9% of FRI cases within the top risk deciles.
  • Key predictors for FRI included frailty, age, previous FRI history, and antidepressant dosage.
  • Low-risk individuals (bottom seven deciles) exhibited minimal FRI incidence (<1.7%).

Conclusions:

  • A practical, time-varying prediction model effectively identifies older adults with depression at high risk for falls and related injuries.
  • This dynamic approach supports clinical decision-making and optimizes the deployment of fall prevention resources.
  • The model's ability to be updated over time enhances its utility for ongoing risk management.