ArticleMathematical biosciences2022
Machine learning for mathematical models of HCV kinetics during antiviral therapy.
Article in Mathematical biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Development of a Machine Learning Model to Predict Short Duration HCV Treatment Response.Journal of viral hepatitis · 2026Article
- Modeling Highlights the Challenge of Achieving and Maintaining Hepatitis C Virus Micro-Elimination Among People Who Inject Drugs.Open forum infectious diseases · 2026Article
- Modeling predicts shortening of DAA treatment duration to 5 weeks in individuals with recent or chronic infection with HCV RNA negativity at day 7 on treatment.The Journal of infectious diseases · 2026Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
- Hybrid model for precise hepatitis-C classification using improved random forest and SVM method.Scientific reports · 2023Article
- Advances in Parameter Estimation and Learning from Data for Mathematical Models of Hepatitis C Viral Kinetics.Mathematics (Basel, Switzerland) · 2022Article
- Modeling-Based Response-Guided DAA Therapy for Chronic Hepatitis C to Identify Individuals for Shortening Treatment Duration.Open forum infectious diseases · 2022Article
- Machine Learning to Enhance Biophysical Models for Riboswitch Discovery.Wiley interdisciplinary reviews. RNAReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 6 institutions in 2 countries.
Funding
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.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.