2 Citations (SciVal)

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 random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months.

OUTCOME MEASURES: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric.

RESULTS: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight 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 the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power.

CONCLUSION: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.

Original languageEnglish
Article number561
Number of pages17
JournalBMC Geriatrics
Volume23
Issue number1
DOIs
Publication statusPublished - 14 Sept 2023

Bibliographical note

Funding Information:
We thank Tamsin Riley, the subject librarian for Pharmacy and Pharmacology at the University of Bath, for assisting in designing the literature search strategy.

Keywords

  • Health informatics
  • Machine learning
  • Model performance evaluation
  • Older adults
  • Predictive modelling
  • Risk management

ASJC Scopus subject areas

  • Geriatrics and Gerontology

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