报告人：陈勇副教授

             宾夕法尼亚大学

题目：Statistical analysis using electronic health records data: challenges and opportunities

时间：2018年6月27日上午09:30

地点：海韵数理楼661

摘要：The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for the study of health conditions, interventions, and outcomes in routine clinical practice. Using EHR data for research facilitates the efficient creation of large research databases, execution of pragmatic clinical trials, and study of rare diseases. Despite these advantages, there are many challenges for research conducted using EHR data. To make valid inference, statisticians must be aware of data generation, capture, and availability issues and utilize appropriate study designs and statistical analysis methods to account for these issues.

In this talk, I will discuss topics related to statistical analysis using EHR data, including data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR data. I will also discuss statistical methods that mitigate some of these issues, including missing data and error in EHR-derived covariates and outcomes. The overarching objective of this talk is to provide participants with an introduction to the structure and content of EHR data as well as a set of appropriate tools to investigate and analyze this rich data resource. This is a joint work with Dr. Rebecca Hubbard and my colleagues at Perelman School of Medicine, University of Pennsylvania.

报告人简介：陈勇博士， 美国宾夕法尼亚大学佩雷尔曼医学院生物统计系终身教授，宾夕法尼亚大学医疗信息学学院资深会员，宾夕法尼亚大学应用数学和计算机中心会员， 宾夕法尼亚大学循证医学中心资深会员，美欧循证综合方法协会当选会员（Elected Member of Society for Research Synthesis Methodology）。 2003年于中国科技大学获得理学学士学位，2010年博士毕业于约翰霍普金斯大学。2010年于德州大学做助理教授；2015年在宾州大学做助理教授，并于2018年提升为副教授和终身教授。多年来从事应用统计学，医疗信息学，生物信息学，循证医学的研究。主要研究方向包括了电子病历数据有偏性校正以及深度分析，动态预测模型，药物安全的信号挖掘和预警，数据驱动的个性化医疗健康管理，多源数据挖掘和整合，以及循证医学。主持和参与了20余项美国大型临床医学项目，发表文章70余篇。当前作为首席研究员负责470万美元的研究项目。

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