Machine learning to predict venous thrombosis in acutely ill medical patients

Patients with an acute medical illness have an increased risk of venous thromboembolism (VTE) during hospitalization that persists following discharge.1, 2 Several randomized trials have demonstrated the efficacy of VTE prophylaxis with direct oral anticoagulants (DOACs) compared to low‐molecular‐weight heparin for 6 to 14 days.3-5 Based on the results of the APEX trial, the US Food and Drug Administration has licensed betrixaban for first‐line thromboprophylaxis in acute medically ill patients at high risk for VTE. The identification of these high‐risk patients may be determined clinically or by use of risk assessment models (RAMs) that rely on integer‐based scoring systems of known risk factors.6, These RAMs demonstrated modest performance in validation data sets.11-13 Machine learning algorithms are constructed to search for patterns in data that provide maximum predictive ability.14, 15 These learning methods have demonstrated superiority to traditional diagnostic and prognostic tools in various domains.16-19