报告人：Donglin Zeng
报告地点：图书馆一楼会议室
报告时间：2019年08月06日星期二14:00-15:00
邀请人：朱文圣
报告摘要：
Advances in technology are revolutionizing medical research by collecting large-scale data from each individual patient (clinical biomarkers, genomics, electronic health records), making it possible to meet the promise of individualized treatment and health care. The availability of these rich data sources provides new opportunities to deeply tailor treatment for each patient, while at the same time, posing tremendous challenges for analyzing highly complex and noisy data in personalized medicine discovery. In this talk, I will present an overview of machine learning methods we have recently developed in this direction: learning methods for the discovery of optimal dynamic treatment regimens, personalized dose finding, benefit-risk analysis, and medical diagnostics. For each method, we establish its theoretical statistical properties including consistency and learning rates. The comparative advantages over existing methods are demonstrated in simulation studies and applications to real world studies.
LEARNING PERSONALIZED TREATMENT
主讲人简介：
Donglin Zeng obtained BS and MS of mathematics from University of Science and Technology of China, and obtained his phd from Department of Biostatistics at University of Michigan in 2001. He has since been a faculty in the department of Biostatistics at the University of North Carolina at Chapel Hill. He is a fellow of both ASA and IMS. His research interest includes semiparametric inference, survival analysis, clinical trials, genetic epidemiology and more recently, machine learning and precision medicine.
