A deep-learning approach to predict bleeding risk over time in patients on extended anticoagulation therapy.

Journal: Journal of thrombosis and haemostasis : JTH
PMID: 38642704

Abstract

BACKGROUND: Thus far, all the clinical models developed to predict major bleeding in patients on extended anticoagulation therapy use the baseline predictors to stratify patients into different risk groups. Therefore, these models do not account for the clinical changes and events that occur after the baseline visit, which can modify risk of bleeding. However, it is difficult to develop predictive models from the routine follow-up clinical interviews, which are irregular sequences of multivariate time series data.

Authors

  • Soroush Shahryari Fard
    The Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
  • Theodore J Perkins
    The Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada; Department of Biochemistry, Microbiology and Immunology, University of Ottawa, Ottawa, Ontario, Canada.
  • Philip S Wells
    The Ottawa Hospital Research Institute, The Ottawa Hospital, Ottawa, Ontario, Canada; Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada. Electronic address: pwells@toh.ca.

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