A Bayesian Reinforcement Learning Framework for Optimizing Sequential Combination Antiretroviral Therapy in People with HIV

Numerous adverse effects (e.g., depression) have been reported for combination antiretroviral therapy (cART) despite its remarkable success on viral suppression in people with HIV (PWH). To improve long-term health outcomes for PWH, there is an urgent need to design personalized optimal cART with the lowest risk of comorbidity in the emerging field of precision medicine for HIV. Large-scale HIV studies offer researchers unprecedented opportunities to optimize personalized cART in a data-driven manner. However, the large number of possible drug combinations for cART makes the estimation of cART effects a high-dimensional combinatorial problem, imposing challenges in both statistical inference and decision-making. We develop a Bayesian reinforcement learning framework for optimizing sequential cART assignments. Applying the proposed approach to a dataset from the Women's Interagency HIV Study, we demonstrate its clinical utility in assisting physicians to make effective treatment decisions, serving the purpose of both viral suppression and comorbidity risk reduction.

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