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UID:20260602T200405EDT-6918w95GoR@132.216.98.100
DTSTAMP:20260603T000405Z
DESCRIPTION:Title: A Bayesian Decision Framework for Optimizing Sequential 
 Combination Antiretroviral Therapy in People with HIV.\n\nAbstract: Numero
 us adverse effects (e.g.\, depression) have been reported for combination 
 antiretroviral therapy (cART) despite its remarkable success on viral supp
 ression in people with HIV (PWH). To improve long-term health outcomes for
  PWH\, there is an urgent need to design personalized optimal cART with th
 e lowest risk of comorbidity in the emerging field of precision medicine f
 or HIV. Large-scale HIV studies offer researchers unprecedented opportunit
 ies to optimize personalized cART in a data-driven manner. However\, the l
 arge number of possible drug combinations for cART makes the estimation of
  cART effects a high-dimensional combinatorial problem\, imposing challeng
 es in both statistical inference and decision-making. We develop a Bayesia
 n reinforcement learning framework for optimizing sequential cART assignme
 nts. Applying the proposed approach to a dataset from the Women’s Interage
 ncy HIV Study\, we demonstrate its clinical utility in assisting physician
 s to make effective treatment decisions\, serving the purpose of both vira
 l suppression and comorbidity risk reduction.\n
DTSTART:20230208T203000Z
DTEND:20230208T213000Z
LOCATION:Room 1140\, 91ºÚÁÏÍø College 2001\, CA\, QC\, Montreal\, H3A 1G1\, 2
 001\, avenue 91ºÚÁÏÍø College
SUMMARY:Yanxun Xu (Johns Hopkins University)
URL:/mathstat/channels/event/yanxun-xu-johns-hopkins-u
 niversity-345863
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