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AMS Weekly Seminar w/ Yanxun Xu on Zoom
April 16, 2020 @ 1:30 pm - 2:30 pm
Title: Decoding When and How to Treat Patients using a Bayesian Probabilistic Reinforcement Learning Approach
Abstract: Patients that undergo kidney transplantation are at risk for a number of complications and graft rejection after surgery, which could lead to death. In order to prevent graft rejection, immuno-suppressive therapy such as tacrolimus is administered to patients post-surgery. The patients are monitored over time with repeated follow-up records (e.g., tacrolimus blood levels, creatinine levels, BMI) after transplantation and the dosage levels of the immunosuppresive drugs can be adjusted by the clinician. Based on patients’ baseline information and the followup data, we develop a Bayesian probabilistic reinforcement learning framework to construct an optimal longitudinal treatment strategy for each individual by combining a longitudinal model for patients’ creatinine levels, a survival model with the endpoint being patient death or graft failure, and a marked point process for clinical decisions (how often the patient is instructed to followup, and drug dosage adjustments). Our method shows promising performance on a real kidney transplantation dataset.
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Topic: AMS Weekly Seminar
Time: Apr 16, 2020 01:30 PM Eastern Time (US and Canada)
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Meeting ID: 907 100 613
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