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

Authors
  • Konerman, Monica A
  • Zhang, Yiwei
  • Zhu, Ji
  • Higgins, Peter D R
  • Lok, Anna S F
  • Waljee, Akbar K
Type
Published Article
Journal
Hepatology
Publisher
Wiley (John Wiley & Sons)
Publication Date
Jun 01, 2015
Volume
61
Issue
6
Pages
1832–1841
Identifiers
DOI: 10.1002/hep.27750
PMID: 25684666
Source
Medline
License
Unknown

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.

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