personalized medicine

Wednesday, November 7, 2018 - 4:30pm - 5:00pm
Yuanjia Wang (Columbia University)
Current guidelines for treatment decision making largely rely on data from ran- domized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large- scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data.
Friday, November 9, 2018 - 9:50am - 10:20am
Donglin Zeng (University of North Carolina, Chapel Hill)
To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the largest benefit can be considered. Recently, a machine learning approach, O-learning, has been proposed to estimate an optimal individualized treatment rule (ITR), but it is developed to make binary decisions and thus limited to compare two treatments.
Wednesday, November 7, 2018 - 10:10am - 10:40am
Thomas Murray (University of Minnesota, Twin Cities)
This talk will describe a new approach for optimizing dynamic treatment regimes that bridges the gap between Bayesian inference and Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model regresses the remaining payoff assuming optimal actions are taken at subsequent stages on the current history and actions.
Thursday, May 31, 2018 - 9:00am - 9:50am
Katarzyna Rejniak (Moffitt Cancer Center)
Systemic chemotherapy is the main anticancer treatment for most kinds of clinically diagnosed solid tumors. However, the efficacy of anticancer drugs is often lower than expected or desired. Moreover, after a good initial reaction, the tumors often become non-responsive and resistant to the drugs. These impediments in anticancer therapy can be attributed to insufficient drug delivery to all tumor cells. If drug molecules are not able to reach all tumor cells in adequate quantities, they cannot evoke the desired effects.
Thursday, September 14, 2017 - 5:00pm - 5:30pm
Yingqi Zhao (Fred Hutchinson Cancer Research Center)
Dynamic treatment regimes (DTRs) are sequential decision regimes for individual patients that can adapt over time to an evolving illness. The goal is to find the DTRs tailored to individual characteristics that lead to the best long term outcome if implemented. In many clinical applications, it is desirable to provide a fixed decision rule over time for the patients.
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