A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients.

ABSTRACT: Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient's risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was

SUBMITTER: Ryan L 

PROVIDER: S-EPMC7907939 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature