Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review.

Journal: Thrombosis and haemostasis
Published Date: Apr 4, 2024

Abstract

BACKGROUND:  Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

Authors

  • Vasiliki Danilatou
    School of Medicine, European University of Cyprus, Nicosia, Cyprus.
  • Dimitrios Dimopoulos
    Department of Physical Medicine and Rehabilitation (PMR), University of Ioannina, Ioannina, Greece.
  • Theodoros Kostoulas
    School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece.
  • James Douketis
    Department of Medicine, McMaster University, Hamilton, Canada.

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