[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118458-en":3,"doc-seo-118458-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118458,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Optimizing anticoagulant use for stroke prevention - a systematic review of machine learning interventions","Machine learning interventions for optimizing anticoagulant use in stroke prevention among people at risk of stroke are systematically identified and evaluated using PRISMA-guided methods. Searches in PubMed and Google Scholar used MeSH terms spanning machine learning, artificial intelligence, anticoagulants, and decision support. From 333 screened articles, 15 met full-text criteria, with extracted evidence on design, samples, models, and focus areas. Model bias is appraised with a prediction risk-of-bias tool.","Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions  \nFeras Almarshad  1, Abdurrahman Mohammad Alshahrani  1, Abdurrahman Saad  \nAl Faiz  1, Aamir Abbas  2, Muhammad Shabbir 1  \nABSTRACT  \nObjectives: To identify and evaluate the use of machine learning interventions for optimizing anticoagulant use in stroke prevention among individuals at risk of stroke.  \nMethods: This systematic review adhered to the PRISMA guidelines. Searches were conducted in PubMed and Google Scholar using MeSH terms such as\"Machine Learning,\" \"Artificial Intelligence,\" \"Anticoagulants,\" and \"Decision Support System.\" Out of 333 screened articles, 36 were shortlisted based on titles, 24 after abstract review, and 15 after full-text evaluation. Included articles focused on machine learning's role in optimizing anticoagulant use for stroke prevention and analyzing primary data. Data were extracted on study design, sample size, machine learning models used, and focus areas. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.  \nResults: Machine learning models, like logistic regression, deep neural networks, random forests, and XGBoost, outperformed traditional scoring systems like CHADS2 and CHA2 DS2-VASc in predicting stroke risk. These models facilitated personalized treatment plans by incorporating genetic and metabolic data for dose optimization. Studies demonstrated the potential for machine learning to improve adherence to stroke prevention strategies, optimize anticoagulant doses, and enhance the rigor of observational studies. However, limitations included reliance on observational data and challenges in external validation and clinical utility assessments.  \nConclusion: Machine learning interventions show promise in optimizing anticoagulant use for stroke prevention, surpassing traditional tools in risk stratification and treatment personalization. However, further research is needed to validate these models in clinical settings and assess their impact on patient outcomes and adherence to stroke prevention strategies.  \nKeywords: Machine Learning ( MeSH) ; Artificial Intelligence ( MeSH) ; Anticoagulants ( MeSH); Stroke Prevention ( Non-MeSH); Atrial Fibrillation ( MeSH); Risk Stratification ( Non-MeSH); Intelligence ( MeSH) .  \nTHIS ARTICLE MAY BE CITED AS: Almarshad F, Alshahrani AM, Faiz ASA, Abbas A, Shabbir M. Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions. Khyber Med Univ J 2024;16(4):334-41. [https://doi.org/10.35845/kmuj.2024.23682](https://doi.org/10.35845/kmuj.2024.23682)  \nINTRODUCTION  \nStroke is an  \nmorbidity and  \nimportant cause of mortality around the  \nworld, as globally, it causes 5.5 million deaths annually.1 Additionally, it has also been reported that around 50% of stroke survivors remain chronically disabled.1 Only in the United States around 700,000 individuals get stroke, contributing to 165,000 deaths on an annual basis.2 The economic burden of stroke ranges from USD 1,810 to 325,108 .3 Stroke risk is increased by conditions like atrial fibrillation and  \nlarge-atherosclerotic disease. Anticoagulants are used to avert the risk of stroke in such around half of these individuals.4 An important challenge occurs when clinicians have to choose the right type of anti-coagulant and select an appropriate dose for that anticoagulant. Therefore, it is important to optimize the choice of these anticoagulants based on several factors specific to the patient, including the relevant comorbidities.  \nIn recent years, the improvement in the ability to gather large amounts of data  \n1: Department of Medicine, College of Medicine, Shaqra University, Saudi Arabia  \n2: Department of Global Health, PHC Global, Pakistan  \nEmail  : aamir.abp@gmai[l.com](l.com)  \n[Contact \\# :](Contact # :) +92-332-2654601  \nDate Submitted: April 28, 2024  \nDate Revised: December 08, 2024 Date Accepted: December 16, 202","cbCaikiNhRYzir9i","https://ap.wps.com/l/cbCaikiNhRYzir9i","pdf",806859,1,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Inclusion and exclusion criteria\n## Search strategy","[{\"question\":\"What is the objective of the systematic review?\",\"answer\":\"To identify and evaluate how machine learning interventions optimize anticoagulant use for stroke prevention in individuals at risk of stroke.\"},{\"question\":\"How were studies selected and assessed for quality?\",\"answer\":\"The review followed PRISMA guidelines. It screened 333 articles down to 15 after full-text evaluation and assessed risk of bias using the Prediction Model Risk of Bias Assessment Tool.\"},{\"question\":\"Which machine learning models were highlighted in the results?\",\"answer\":\"Logistic regression, deep neural networks, random forests, and XGBoost were reported to outperform traditional scoring systems such as CHADS2 and CHA2 DS2-VASc for stroke risk prediction.\"}]","Optimizing anticoagulant use for stroke prevention - a systematic review of machine learning interventions | PDF",1785683709,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"optimizing-anticoagulant-use-for-stroke-prevention-a-systematic-review-of-machine-learning-interventions","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/optimizing-anticoagulant-use-for-stroke-prevention-a-systematic-review-of-machine-learning-interventions/118458/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the objective of the systematic review?","Question",{"text":74,"@type":75},"To identify and evaluate how machine learning interventions optimize anticoagulant use for stroke prevention in individuals at risk of stroke.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were studies selected and assessed for quality?",{"text":79,"@type":75},"The review followed PRISMA guidelines. It screened 333 articles down to 15 after full-text evaluation and assessed risk of bias using the Prediction Model Risk of Bias Assessment Tool.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning models were highlighted in the results?",{"text":83,"@type":75},"Logistic regression, deep neural networks, random forests, and XGBoost were reported to outperform traditional scoring systems such as CHADS2 and CHA2 DS2-VASc for stroke risk prediction.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]