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

Juan Lu, Rebecca Hutchens, Joseph Hung, Mohammed Bennamoun, Brendan McQuillan, Tom Briffa, Ferdous Sohel, Kevin Murray, Jonathon Stewart, Benjamin Chow, Frank Sanfilippo, Girish Dwivedi
2022 arXiv   pre-print

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https://web.archive.org/web/20220207070005/https://arxiv.org/ftp/arxiv/papers/2202/2202.01975.pdf

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Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.  ...  Appropriate antithrombotic therapy for patients with atrial fibrillation (AF) requires assessment of ischemic stroke and bleeding risks.  ...  Staff at the WA Data Linkage Branch and data custodians of the WA Department of Health Inpatient Data Collections and Registrar General for access to and provision of the State linked data.  ... 
arXiv:2202.01975v1 fatcat:cv7263whlrddbhbx6woapje4ru
Citation

Juan Lu, Rebecca Hutchens, Joseph Hung, Mohammed Bennamoun, Brendan McQuillan, Tom Briffa, Ferdous Sohel, Kevin Murray, Jonathon Stewart, Benjamin Chow, Frank Sanfilippo, Girish Dwivedi. "Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation." arXiv (2022)