Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis.

Abstract

Abstract Background Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. Design: Systematic review and meta-analyses. Participants: Older adults (≥ 65 years) in any setting. Intervention: Machine learning models for predicting clinical outcomes in older adults were evaluated. A meta-analysis was conducted where the predictive models were compared based on their performance in predicting mortality. Outcome measures: Studies were grouped by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. Results 29 studies that satisfied the systematic review criteria were appraised and six studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from six studies included in the meta-analysis yielded a summary estimate of 0.82 (95%CI: 0.76–0.87), signifying good discriminatory power in predicting mortality. Conclusion The meta-analysis indicates that machine learning models can predict mortality. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; they should be integrated into a larger research setting to predict various clinical outcomes.

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