From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management

AbstractThe high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this novel technology to process large amounts of high-dimensional data can help identify new risk factors and better risk stratify patients for thromboprophylaxis. Applications of ML for VTE include systems that interpret medical imaging, assess the severity of the VTE, tailor treatment according to individual patient needs, and identify VTE cases to facilitate su…