Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence - Summary - MDSpire
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Machine Learning and Deep Learning Models for Predicting Future Falls in Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Longitudinal Evidence
To systematically summarize ML- and DL-based prediction models for future falls in community-dwelling older adults, evaluate their predictive performance, assess methodological quality and risk of bias, and synthesize discrimination performance.
Key Findings:
Approximately 26% of older adults experience at least one fall annually, leading to significant health concerns and burden on healthcare systems.
Traditional fall risk assessment tools may not fully capture the multifactorial nature of falls.
Machine learning methods can improve fall risk prediction by integrating heterogeneous predictors and accommodating complex relationships.
Interpretation:
Machine learning and deep learning approaches have the potential to enhance the prediction of falls in community-dwelling older adults, addressing limitations of traditional models.
Limitations:
Existing reviews primarily focused on institutionalized settings or real-time detection rather than community-based prediction, limiting the applicability of findings.
Limited sample sizes and generalizability of some ML and DL models.
Conclusion:
The review highlights the urgent need for comprehensive evaluation of ML-based models to improve fall prevention strategies in community-dwelling older adults.
