BMI Candidate Xiaoyan Wang Ph.D Candidate

BMI Candidate
Xiaoyan Wang
Ph.D Candidate
Columbia University
Seminar information:
Date – Friday, July 16th
Time – 12:00
Location – ITE, room 336
Quantitative Pharmacovigilance using Natural Language Processing,
Statistics and Electronic Health Records
Adverse drug events (ADEs) cause public health problems worldwide. About 10% of
ADEs are estimated to cause permanent disability. In the United States alone, ADEs
cause more than 770,000 injuries or deaths each year. Therefore, establishing accurate
and timely safety profiles over the market life of a drug is critical for patient safety. For a
long time, pharmacovigilance researchers have striven to develop a real time, continuous
and prospective approach. Toward this goal, I propose a framework for a high throughput
system that demonstrates the relevance and significance of using unstructured data from
an electronic health record (EHR) system for pharmacovigilance. The framework
consists of three components that utilize natural language processing (NLP), statistics,
information theory, and narrative reports from an EHR. The research presented in this
talk has produced several novel findings and provided new solutions towards the
challenging problem of pharmacovigilance. To the best of our knowledge, this is the first
study demonstrating the use of unstructured EHR patient data for pharmacovigilance. In
conclusion, this talk describes a framework for the development of automated, active and
prospective pharmacovigilance which could potentially unveil drug safety profiles and
novel adverse events in a timely fashion.
Xiaoyan Wang's research is focused on clinical/translational informatics research using
electronic health records (EHR). Her main research interests are the development of an
automated and high throughput framework of pharmacovigilance and syndromic
surveillance, the design of large scale text mining systems, and medical quality research
in translational informatics. Xiaoyan is a Ph.D. candidate from the department of
biomedical informatics at Columbia University. She received a master’s degree in public
health informatics from Columbia University and a master’s degree in genetics from the
University of Kansas.