[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121648-en":3,"doc-seo-121648-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121648,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning and the Conundrum of Stroke Risk Prediction - Review","Stroke is a leading cause of death worldwide, and rising healthcare costs make early, non-invasive stroke risk stratification essential. Current risk assessment relies mainly on clinical factors and comorbidities, with regression-based algorithms offering only moderate predictive accuracy. This review synthesizes recent machine learning approaches for stroke risk prediction and for clarifying mechanisms, including comparisons against conventional statistics and ML integrated with multiscale computational modelling to better capture thrombogenesis.","Arrhythmia  \nREVIEW  \nRisk and Stratification  \nMachine Learning and the Conundrum of Stroke Risk Prediction  \nYaacoub Chahine ,1 Matthew J Magoon ,2 Bahetihazi Maidu ,3 Juan C del Álamo  3,4,5  \n,  \nPatrick M Boyle 2,4,5 and Nazem Akoum 1,2  \n1. Division of Cardiology, University of Washington, Seattle, WA, US; 2. Department of Bioengineering, University of Washington, Seattle, WA, US; 3. Department of Mechanical Engineering, University of Washington, Seattle, WA, US; 4. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, US; 5. Center for Cardiovascular Biology, University of Washington, Seattle, WA, US  \nAbstract  \nStroke 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.  \nKeywords  \nCardiovascular disease, computational modelling, neural networks, atrial fibrillation, thromboembolism, computational fluid dynamics, multiscale modelling  \nAcknowledgements: PMB and NA contributed equally.  \nDisclosure: The authors have no conflicts of interest to declare.  \nFunding: 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.  \nReceived: 13 October 2022 Accepted: 7 February 2023 Citation: Arrhythmia & Electrophysiology Review 2023;12:e07. DOI: [https://doi.org/10.15420/aer.2022.34](https://doi.org/10.15420/aer.2022.34)[ ](https://doi.org/10.15420/aer.2022.34)[Correspondence:](Correspondence: Patrick M Boyle)[ Patrick M Boyle](Correspondence: Patrick M Boyle), Department of Bioengineering, University of Washington, 3720 15th Ave NE N361, UW Mailbox 355061, Seattle, WA 98195, US.  \nE: [pmjboyle@uw.edu](pmjboyle@uw.edu) and Nazem Akoum, Division of Cardiology, University of Washington, 1959 NE Pacific Street, Seattle, WA 98195, US.  \nE: [nakoum@cardiology.washington.edu](nakoum@cardiology.washington.edu)  \nOpen Access: This work is open access under the CC-BY-NC 4.0 License which allows users to copy, redistribute and make derivative works for non-commercial purposes, provided the original work is cited correctly.  \nStroke is a leading cause of death and permanent disability worldwide 1 Ischaemic stroke is the most common stroke variety, comprising more than 80% of strokes in the US.2 One mechanism of ischaemic stroke is atherosclerosis in the extracranial and intracranial arteries, with plaque rupture leading to thrombosis. The second major category is embolic stroke, in which thrombi form in the heart or the arterial/venous beds and then embolise to occlude downstream arteries, typically in the intracranial domain.3 Most embolic strokes are associated with AF. Understanding stroke mechanisms and developing effective risk stratification strategies are crucial for primary and ","cbCaitcKXwDkGnUj","https://ap.wps.com/l/cbCaitcKXwDkGnUj","pdf",707788,1,9,"English","en",105,"# Risk and Stratification\n## Stroke mechanisms and epidemiology\n## Limitations of conventional regression models\n## Machine learning approaches for stroke risk prediction\n## Multiscale computational modelling and thrombogenesis","[{\"question\":\"Why is early, non-invasive stroke risk stratification important?\",\"answer\":\"Stroke causes death and permanent disability worldwide, and escalating healthcare costs increase the need for earlier risk identification so that timely interventions can be applied.\"},{\"question\":\"What limitation do regression-based stroke risk algorithms have?\",\"answer\":\"They use clinical risk factors and comorbidities with regression-based statistical associations, which tend to have moderate predictive accuracy and oversimplify complex relationships.\"},{\"question\":\"How does machine learning improve stroke risk prediction?\",\"answer\":\"Machine learning can model hidden, complex relationships across multiple clinical and physiological variables, potentially enabling more patient-specific and personalized predictions than standard regression methods.\"}]","Machine Learning and the Conundrum of Stroke Risk Prediction - 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