"Machine learning for mathematical models of HCV kinetics during antivi" by Alexander Churkin, Stephanie Kriss et al.
 

Machine learning for mathematical models of HCV kinetics during antiviral therapy

Document Type

Article

Publication Date

1-1-2022

Abstract

Mathematical models for hepatitis C virus (HCV) dynamics have provided a means for evaluating the antiviral effectiveness of therapy and estimating treatment outcomes such as the time to cure. Recently, a mathematical modeling approach was used in the first proof-of-concept clinical trial assessing in real-time the utility of response-guided therapy with direct-acting antivirals (DAAs) in chronic HCV-infected patients. Several retrospective studies have shown that mathematical modeling of viral kinetics predicts time to cure of less than 12 weeks in the majority of individuals treated with sofosbuvir-based as well as other DAA regimens. A database of these studies was built, and machine learning methods were evaluated for their ability to estimate the time to cure for each patient to facilitate real-time modeling studies. Data from these studies exploring mathematical modeling of HCV kinetics under DAAs in 266 chronic HCV-infected patients were gathered. Different learning methods were applied and trained on part of the dataset (‘train’ set), to predict time to cure on the untrained part (‘test’ set). Our results show that this machine learning approach provides a means for establishing an accurate time to cure prediction that will support the implementation of individualized treatment.

Identifier

85121824220 (Scopus)

Publication Title

Mathematical Biosciences

External Full Text Location

https://doi.org/10.1016/j.mbs.2021.108756

e-ISSN

18793134

ISSN

00255564

PubMed ID

34883104

Volume

343

Grant

R01AI078881

Fund Ref

National Institutes of Health

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