Affordable Access

Publisher Website

Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data.

Authors
Type
Published Article
Journal
Hepatology
0270-9139
Publisher
Wiley Blackwell (John Wiley & Sons)
Publication Date
Volume
61
Issue
6
Pages
1832–1841
Identifiers
DOI: 10.1002/hep.27750
PMID: 25684666
Source
Medline

Abstract

Prediction models that incorporate longitudinal data can capture nonlinear disease progression in chronic hepatitis C and thus outperform baseline models. Machine learning methods can capture complex relationships between predictors and outcomes, yielding more accurate predictions; our models can help target costly therapies to patients with the most urgent need, guide the intensity of clinical monitoring required, and provide prognostic information to patients.

There are no comments yet on this publication. Be the first to share your thoughts.

Statistics

Seen <100 times
0 Comments
F