Machine learning for mathematical models of HCV kinetics during antiviral therapy

DOI10.1016/j.mbs.2021.108756zbMath1486.92107OpenAlexW4200212321MaRDI QIDQ2118485

Harel Dahari, Ohad Etzion, Ashish Goyal, Asher Uziel, Scott J. Cotler, Horacio G. Rotstein, Amir Shlomai, Stephanie Kriss, Rami Zakh, Danny Barash, Alexander Churkin

Publication date: 22 March 2022

Published in: Mathematical Biosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mbs.2021.108756

zbMATH Keywords

mathematical modelingmachine learningviral dynamicshepatitis C virusdirect-acting antivirals

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Medical epidemiology (92C60)

Cites Work

• Unnamed Item
• HIV-1 infection and low steady state viral loads
• Modeling HIV dynamics and antiviral response with consideration of time-varying drug exposures, adherence and phenotypic sensitivity
• Numerical schemes for solving and optimizing multiscale models with age of hepatitis C virus dynamics
• Nonlinear diffusion filtering on extended neighborhood
• A parameter estimation method for multiscale models of hepatitis C virus dynamics
• Modeling hepatitis C virus dynamics: liver regeneration and critical drug efficacy
• On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• A method for the solution of certain non-linear problems in least squares
• Random forests

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