Machine learning to predict venous thrombosis in acutely ill medical patients.

ABSTRACT:

Background

The identification of acutely ill patients at high risk for venous thromboembolism (VTE) may be determined clinically or by use of integer-based scoring systems. These scores demonstrated modest performance in external data sets.

Objectives

To evaluate the performance of machine learning models compared to the IMPROVE score.

Methods

The APEX trial randomized 7513 acutely medically ill patients to extended duration betrixaban vs. enoxaparin. Including 68 variables, a super learner model (ML) was built to predict VTE by combining estimates from 5 families of candidate models. A "reduced" model (rML) was also developed using 16 variables that were thought, a priori, to be associated with VTE. The IMPROVE score was calculated for each patient. Model performance

SUBMITTER: Nafee T 

PROVIDER: S-EPMC7040551 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature