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Appropriate antithrombotic therapy for patients with atrial fibrillation (AF)requires assessment of ischemic stroke and bleeding risks. However, riskstratification schemas such as CHA2DS2-VASc and HAS-BLED have modest predictivecapacity for patients with AF. Machine learning (ML) techniques may improvepredictive performance and support decision-making for appropriateantithrombotic therapy. We compared the performance of multilabel ML modelswith the currently used risk scores for predicting outcomes in AF patients.Materials and Methods This was a retrospective cohort study of 9670 patients,mean age 76.9 years, 46% women, who were hospitalized with non-valvular AF, andhad 1-year follow-up. The primary outcome was ischemic stroke and majorbleeding admission. The secondary outcomes were all-cause death and event-freesurvival. The discriminant power of ML models was compared with clinical riskscores by the area under the curve (AUC). Risk stratification was assessedusing the net reclassification index. Results Multilabel gradient boostingmachine provided the best discriminant power for stroke, major bleeding, anddeath (AUC = 0.685, 0.709, and 0.765 respectively) compared to other ML models.It provided modest performance improvement for stroke compared to CHA2DS2-VASc(AUC = 0.652), but significantly improved major bleeding prediction compared toHAS-BLED (AUC = 0.522). It also had a much greater discriminant power for deathcompared with CHA2DS2-VASc (AUC = 0.606). Also, models identified additionalrisk features (such as hemoglobin level, renal function, etc.) for eachoutcome. Conclusions Multilabel ML models can outperform clinical riskstratification scores for predicting the risk of major bleeding and death innon-valvular AF patients.

Performance of multilabel machine learning models and risk  stratification schemas for predicting stroke and bleeding risk in patients  with non-valvular atrial fibrillation