Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions
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...
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Khyber Medical University
2024-12-01
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| Series: | Khyber Medical University Journal |
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| Online Access: | https://www.kmuj.kmu.edu.pk/article/view/23682 |
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| author | Feras Almarshad Abdurrahman Mohammad Alshahrani Abdurrahman Saad Al Faiz Aamir Abbas Muhammad Shabbir |
| author_facet | Feras Almarshad Abdurrahman Mohammad Alshahrani Abdurrahman Saad Al Faiz Aamir Abbas Muhammad Shabbir |
| author_sort | Feras Almarshad |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b9fa66e18fa947dfac53cba4b47de302 |
| institution | DOAJ |
| issn | 2305-2643 2305-2651 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Khyber Medical University |
| record_format | Article |
| series | Khyber Medical University Journal |
| spelling | doaj-art-b9fa66e18fa947dfac53cba4b47de3022025-08-20T03:03:05ZengKhyber Medical UniversityKhyber Medical University Journal2305-26432305-26512024-12-011643344110.35845/kmuj.2024.236822454Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventionsFeras Almarshad0https://orcid.org/0000-0002-9468-1489Abdurrahman Mohammad Alshahrani1https://orcid.org/0000-0001-8814-9333Abdurrahman Saad Al Faiz2https://orcid.org/0009-0005-9488-0516Aamir Abbas3https://orcid.org/0000-0002-2634-2063Muhammad Shabbir4https://orcid.org/0000-0001-7449-8399Department of Medicine, College of Medicine, Shaqra University, Saudi ArabiaDepartment of Medicine, College of Medicine, Shaqra University, Saudi Arabia Department of Medicine, College of Medicine, Shaqra University, Saudi ArabiaDepartment of Global Health, PHC Global, PakistanDepartment of Medicine, College of Medicine, Shaqra University, Saudi ArabiaObjective: 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.https://www.kmuj.kmu.edu.pk/article/view/23682machine learningstrokepredictive modelingartificial intelligenceanticoagulantsstroke preventionatrial fibrillationrisk stratificationintelligence |
| spellingShingle | Feras Almarshad Abdurrahman Mohammad Alshahrani Abdurrahman Saad Al Faiz Aamir Abbas Muhammad Shabbir Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions Khyber Medical University Journal machine learning stroke predictive modeling artificial intelligence anticoagulants stroke prevention atrial fibrillation risk stratification intelligence |
| title | Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions |
| title_full | Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions |
| title_fullStr | Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions |
| title_full_unstemmed | Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions |
| title_short | Optimizing anticoagulant use for stroke prevention: a systematic review of machine learning interventions |
| title_sort | optimizing anticoagulant use for stroke prevention a systematic review of machine learning interventions |
| topic | machine learning stroke predictive modeling artificial intelligence anticoagulants stroke prevention atrial fibrillation risk stratification intelligence |
| url | https://www.kmuj.kmu.edu.pk/article/view/23682 |
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