Optimal STI controls for HIV patients based on an efficient deep Q learning method

DOI10.1016/j.jtbi.2024.111914zbMATH Open1548.92027MaRDI QIDQ6623784

Hee-Dae Kwon, Myungjoo Kang, Jaemoo Choi, Changyeon Yoon

Publication date: 24 October 2024

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

zbMATH Keywords

reinforcement learninghuman immunodeficiency virus (HIV)deep Q networksprioritized experience replaystructured treatment interruption (STI)

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Nonlinear ordinary differential equations and systems (34A34) Medical applications (general) (92C50)

Cites Work

• HIV-1 infection and low steady state viral loads
• Dynamic multidrug therapies for HIV: Optimal and STI control approaches
• HIV dynamics: Modeling, data analysis, and optimal treatment protocols

This page was built for publication: Optimal STI controls for HIV patients based on an efficient deep Q learning method