Application of Machine Learning Approaches in Predicting Clinical Outcomes in Older Adults - A Systematic Review and Meta-Analysis

Aug 4, 2026

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Machine learning-based prediction models offer meaningful potential benefits for geriatric care. This systematic review and meta-analysis evaluated machine learning models that predict clinical outcomes in older adults (≥65 years) across any setting. Outcomes were assessed by comparing predictive performance using the area under the receiver operating characteristic curve, focusing on mortality within and beyond 6 months. Results included eight mortality studies pooled from 37 eligible studies, showing good discriminatory power (AUC ~0.80–0.81).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Background

Methods

Outcomes and Results

Conclusion

Background

Burden of illness in older adults

Limitations of traditional statistics

Rationale for machine learning

Citation for published version: Olender, RT, Roy, S & Nishtala, PS 2023, 'Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis', BMC Geriatrics, vol. 23, no. 1, 561. [https://doi.org/10.1186/s12877-023-04246-w](https://doi.org/10.1186/s12877-023-04246-w) DOI: 10.1186/s12877-023-04246-w Publication date: 2023 Document Version Publisher's PDF, also known as Version of record Link to publication Publisher Rights CC BY University of Bath Alternative formats If you require this document in an alternative format, please contact: [openaccess@bath.ac.uk](openaccess@bath.ac.uk) General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 03. Aug. 2026 Olender et al. BMC Geriatrics (2023) 23:561 [https://doi.org/10.1186/s12877-023-04246-w](https://doi.org/10.1186/s12877-023-04246-w) BMC Geriatrics RESEARCH Open Access Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis Robert T. Olender 1*, Sandipan Roy2 and Prasad S. Nishtala3 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 randomeffects 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. Keywords Older adults, Machine learning, Predictive modelling, Model performance evaluation, Health informatics, Risk management *Correspondence: Robert T. Olender [rto20@bath.ac.uk](rto20@bath.ac.uk) 1 Department of Life Sciences, University of Bath, Bath BA2 7AY, UK 2 Department of Mathematical Sciences, University of Bath, Bath BA2 7AY, UK 3 Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath BA2 7AY, UK Background Older adults aged ≥65 years are the highest healthcare consumers, accounting for the largest and disproportionate share of hospitalisations an

FAQs

What population and clinical outcomes were evaluated in the review?

The review focused on older adults aged ≥65 years and evaluated machine learning models predicting clinical outcomes, with pooled attention on mortality outcomes.

How were the prediction models compared across studies?

Studies were grouped by outcome, and performance was compared using the area under the receiver operating characteristic curve (AUC).

What did the meta-analysis find about mortality prediction?

Pooling mortality studies showed good discriminatory power, with summary AUC estimates of about 0.80 for mortality within 6 months and about 0.81 for mortality over 6 months.

Aug 4, 2026

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