報告題目：Novel Machine Learning based Research on Dynamic Decision Trees Leading Toward Personalized Health Care and Precision Medicine
報告人：王璐 教授
單位：美國密歇根大學(xué)
時間：2018年8月21日（周二）下午3點(diǎn)
地點(diǎn)：翡翠湖校區(qū)逸夫科技樓A座第一會議室
摘要：Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. We develop robust and flexible semiparametric and machine learning methods for estimating optimal DTRs. In this talk, we present a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. ACWL can handle multiple treatments at each stage and does not require prespecifying candidate DTRs. At each stage, we develop robust semiparametric regression-based contrasts with the adaptation of treatment effect ordering for each patient, and the adaptive contrasts simplify the problem of optimization with multiple treatment comparisons to a weighted classification problem that can be solved with existing machine learning techniques. We further develop a tree-based reinforcement learning (T-RL) method to directly estimate optimal DTRs in a multi-stage multi-treatment setting. At each stage, T-RL builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through the purity measure constructed with augmented inverse probability weighted estimators. By combining robust semiparametric regression with flexible tree-based learning, T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust to tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. We illustrate the performances of both methods in simulations and case studies.
報告人簡介：
Professor Lu Wang received her Ph.D from Harvard University in 2008 and joined the faculty at the University of Michigan in the same year. Dr. Wang's research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, nonparametric and semiparametric regressions, missing data analysis, functional data analysis, and longitudinal (correlated/clustered) data analysis. She has published 66 papers on top-tier jounals, and has been closely collaborating with investigators at M.D. Anderson Cancer Center, University of Michigan Medical School, and Harvard School of Public Health.
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