学术报告—“Anonymizing and Sharing Medical Text Records”

2016年06月17日 09:59  点击:[]

应西安交通大学信息工程科学研究中心的邀请, 美国University of Massachusetts Lowell的Xiaobai Li(李晓白)教授将于2016年6月20日下午2:30在电信学院第一会议室(西一楼B段344房间)作题为

“Anonymizing and Sharing Medical Text Records”



Dr. Xiaobai Li is a Professor of Information Systems in the Department of Operations and Information Systems at the University of Massachusetts Lowell, USA. He received his Ph.D. in management science from the University of South Carolina. Dr. Li’s research focuses on data mining and analytics, data privacy, and information economics. He has received funding for his research from National Institutes of Health (NIH) and National Science Foundation (NSF), USA. His work has appeared in Management Science, Information Systems Research, MIS Quarterly, Operations Research, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics, Communications of the ACM, INFORMS Journal on Computing, Decision Support Systems, and European Journal of Operational Research, among others.


Health information technology has increased accessibility of health and medical data and benefited medical research and healthcare management. However, there are rising concerns about patient privacy in sharing medical and healthcare data. A large amount of these data are in free text form. Existing techniques for privacy-preserving data sharing deal largely with structured data. Current privacy approaches for medical text data focus on detection and removal of patient identifiers from the data, which may be inadequate for protecting privacy or preserving data quality. We propose a new systematic approach to extract, cluster, and anonymize medical text records. Our approach integrates methods developed in both data privacy and health informatics fields. The key novel elements of our approach include a recursive partitioning method to cluster medical text records based on the similarity of the health and medical information, and a value-enumeration method to anonymize potentially identifying information in the text data. An experimental study is conducted using real-world medical documents. The results of the experiments demonstrate the effectiveness of the proposed approach.

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