2024 journal article
An XGBoost-Based Fitted Q Iteration for Finding the Optimal STI Strategies for HIV Patients
IEEE Transactions on Neural Networks and Learning Systems.
author keywords: Viruses (medical); Immune system; Drugs; Numerical models; Subspace constraints; Mathematical models; Load modeling; Fitted Q iteration algorithm; human immunodeficiency virus (HIV); reinforcement learning; structured treatment interruption (STI) strategy; XGBoost regression
TL;DR:
An XGBoost-based fitted Q iteration algorithm is proposed for finding the optimal structured treatment interruption (STI) strategies for HIV patients, and it is shown that this algorithm can obtain acceptable and optimal STI strategies with fewer training data.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(OpenAlex)
Source: ORCID
Added: August 3, 2022
The computational algorithm proposed in this article is an important step toward the development of computational tools that could help guide clinicians to personalize the management of human immunodeficiency virus (HIV) infection. In this article, an XGBoost-based fitted Q iteration algorithm is proposed for finding the optimal structured treatment interruption (STI) strategies for HIV patients. Using the XGBoost-based fitted Q iteration algorithm, we can obtain acceptable and optimal STI strategies with fewer training data, when compared with the extra-tree-based fitted Q iteration algorithm, deep Q-networks (DQNs), and proximal policy optimization (PPO) algorithm. In addition, the XGBoost-based fitted Q iteration algorithm is computationally more efficient than the extra-tree-based fitted Q iteration algorithm.
