Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions
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Abstract
Objective: To identify and evaluate the use of machine learning interventions for optimizing anticoagulant use in stroke prevention among individuals at risk of stroke.
Methods: 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.
Results: Machine learning models, like logistic regression, deep neural networks, random forests, and XGBoost, outperformed traditional scoring systems like CHADS2 and CHA2DS2-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.
Conclusion: 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.
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