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DTSTAMP:20260602T182355Z
DESCRIPTION:Title: A Bayesian Decision Framework for Optimizing Sequential 
 Combination Antiretroviral Therapy in People with HIV. \n\nAsbtract: Numer
 ous adverse effects (e.g.\, depression) have been reported for combination
  antiretroviral therapy (cART) despite its remarkable success on viral sup
 pression in people with HIV (PWH). To improve long-term health outcomes fo
 r PWH\, there is an urgent need to design personalized optimal cART with t
 he lowest risk of comorbidity in the emerging field of precision medicine 
 for HIV. Large-scale HIV studies offer researchers unprecedented opportuni
 ties to optimize personalized cART in a data-driven manner. However\, the 
 large number of possible drug combinations for cART makes the estimation o
 f cART effects a high-dimensional combinatorial problem\, imposing challen
 ges in both statistical inference and decision-making. We develop a Bayesi
 an reinforcement learning framework for optimizing sequential cART assignm
 ents. Applying the proposed approach to a dataset from the Women’s Interag
 ency HIV Study\, we demonstrate its clinical utility in assisting physicia
 ns to make effective treatment decisions\, serving the purpose of both vir
 al suppression and comorbidity risk reduction.\n	\n	https://www.ams.jhu.edu/
 ~yxu70/\n\nWeb site : /epi-biostat-occh/seminars-even
 ts/seminars/biostatistics\n\nVia Zoom only\n\n \n
DTSTART:20230208T203000Z
DTEND:20230208T213000Z
SUMMARY:Yanxun Xu (Johns Hopkins University)
URL:/mathstat/channels/event/yanxun-xu-johns-hopkins-u
 niversity-345749
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