Machine Learning and the Conundrum of Stroke Risk Prediction.

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

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Copyright © 2023, Radcliffe Cardiology.

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Disclosure: The authors have no conflicts of interest to declare. Funding: This work was supported by the John Locke Charitable Trust to NA; NIH R01-HL158667 to JCA, PMB and NA; NIH R01-HL160024 to JCA; and a Collaboration Innovation Award from the Institute of Translational Health Science (ITHS) grant support (UL1 TR-002319 NCATS/NIH) to PMB and NA.