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UID:20250724T192334EDT-1503uIAPsD@132.216.98.100
DTSTAMP:20250724T232334Z
DESCRIPTION:Yanxun Xu\, PhD\n\nAssociate Professor\, Johns Hopkins Universi
 ty\n\nWhere: Hybrid Event | 2001 ÐãÉ«Ö±²¥ College\, Room 1140\; Zoom\n\nAbst
 ract\n\nNumerous adverse effects (e.g.\, depression) have been reported fo
 r combination antiretroviral therapy (cART) despite its remarkable success
  on viral suppression in people with HIV (PWH). To improve long-term healt
 h outcomes for PWH\, there is an urgent need to design personalized optima
 l cART with the lowest risk of comorbidity in the emerging field of precis
 ion medicine for HIV. Large-scale HIV studies offer researchers unpreceden
 ted opportunities to optimize personalized cART in a data-driven manner. H
 owever\, the large number of possible drug combinations for cART makes the
  estimation of cART effects a high-dimensional combinatorial problem\, imp
 osing challenges in both statistical inference and decision-making. We dev
 elop a Bayesian reinforcement learning framework for optimizing sequential
  cART assignments. Applying the proposed approach to a dataset from the Wo
 men’s Interagency HIV Study\, we demonstrate its clinical utility in assis
 ting physicians to make effective treatment decisions\, serving the purpos
 e of both viral suppression and comorbidity risk reduction.\n\nSpeaker Bio
 \n\nhttps://www.ams.jhu.edu/~yxu70/\n\n \n
DTSTART:20230208T203000Z
DTEND:20230208T213000Z
SUMMARY:A Bayesian Decision Framework for Optimizing Sequential Combination
  Antiretroviral Therapy in People with HIV
URL:/epi-biostat-occh/channels/event/bayesian-decision
 -framework-optimizing-sequential-combination-antiretroviral-therapy-people
 -hiv-344478
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