Machine learning models to predict disease progression among veterans with hepatitis C virus.

ABSTRACT:

Background

Machine learning (ML) algorithms provide effective ways to build prediction models using longitudinal information given their capacity to incorporate numerous predictor variables without compromising the accuracy of the risk prediction. Clinical risk prediction models in chronic hepatitis C virus (CHC) can be challenging due to non-linear nature of disease progression. We developed and compared two ML algorithms to predict cirrhosis development in a large CHC-infected cohort using longitudinal data.

Methods and findings

We used national Veterans Health Administration (VHA) data to identify CHC patients in care between 2000-2016. The primary outcome was cirrhosis development ascertained by two consecutive aspartate aminotransferase (AST)-to-platelet ratio indexes (

SUBMITTER: Konerman MA 

PROVIDER: S-EPMC6319806 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature