Why the study?
Machine-learning techniques are increasingly used to predict healthcare outcomes in cardiac rehabilitation, but a critical appraisal of existing ML-based prognosis predictive models and identification of key research gaps were needed.
Can machine-learning based prediction models accurately predict healthcare outcomes in patients participating in cardiac rehabilitation?
Population
22 ML-based clinical models from 7 studies across multiple phases of CR
Design
Systematic review
Authors
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ML models warrant caution for clinical use absent validation; leaves open their adoption pending external testing.
Can machine-learning based prediction models accurately predict healthcare outcomes in patients participating in cardiac rehabilitation?
While machine learning models show good predictive capabilities for cardiac rehabilitation outcomes, their readiness for clinical implementation is questionable due to a lack of external validation and a high risk of bias.
Tieliwaerdi et al. (2024) studied this question.