Towards A Self-Learning EHR System
报告题目： Towards A Self-Learning EHR System
报告人： 蔡天西教授（哈佛大学）
时间地点：2019年09月02日(星期一)下午4:00-
紫金港校区管理学院行政楼14楼1417报告厅
摘要：The wide adoption of electronic health records (EHR) systems has led to the availability of large clinical datasets available for discovery research. EHR data, linked with bio-repository, is a valuable new source for deriving real-word, data-driven prediction models of disease risk and progression. Yet, they also bring analytical difficulties. Precise information on clinical outcomes is not readily available and requires labor intensive manual chart review. Synthesizing information across healthcare systems is also challenging due to heterogeneity and privacy. In this talk, I’ll discuss analytical approaches for mining EHR data with a focus on scalability, reproducibility and automated knowledge extraction. These methods will be illustrated using EHR data from Partner’s Healthcare and Veteran Health Administration.
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联系人: 张立新教授 stazlx@zju.edu.cn
浙江大学数据科学研究中心
报告人简介：
l Professor of Biomedical Informatics, Harvard Medical School
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l John Rock Professor of Population and Translational Data Sciences, Harvard T.H. Chan School of Public Health
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l Director,Translational Data Science Center for a Learning Health System (CELEHS)
l Tianxi Cai is a major player in developing analytical tools for EHR phenotyping and predictive modeling with biomedical data. She provides statistical leadership on several large-scale projects, including the NIH-funded BD2K PIC-SURE Center of Excellence and N-GRID Center for Excellence in Genomic Science to study neuropsychiatric disease at DBMI. In addition to her collaborative work, Cai's research lab in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health develops novel statistical methods for personalized medicine using genomic and phenomic data. Cai received her ScD in Biostatistics at Harvard and was an assistant professor at the University of Washington before returning to Harvard as a faculty member in 2002.