Prediction of Venous Thromboembolism in Diverse Populations Using Machine Learning and Structured Electronic Health Records.

ABSTRACT:

Background

Venous thromboembolism (VTE) is a major cause of morbidity and mortality worldwide. Current risk assessment tools, such as the Caprini and Padua scores and Wells criteria, have limitations in their applicability and accuracy. This study aimed to develop machine learning models using structured electronic health record data to predict diagnosis and 1-year risk of VTE.

Methods

We trained and validated models on data from 159 001 participants in the Mount Sinai Data Warehouse. We then externally tested them on 401 723 participants in the UK Biobank and 123 039 participants in All of Us. All data sets contain populations of diverse ancestries and clinical histories. We used these data sets to develop small, medium, and large models with increasing features on a range

SUBMITTER: Chen R 

PROVIDER: S-EPMC10872966 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature