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Published on: May 15, 2020
Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a
Xiaoyan Wang1, George Hripcsak, Marianthi Markatou
1Department of Biomedical Informatics, Columbia University, 622 West 168 Street, VC5, New York, NY 10032, USA. friedman@dbmi.columbia.edu
Summary
This study shows that using natural language processing (NLP) with electronic health records (EHR) can effectively detect drug side effects. This framework offers a feasible approach for active pharmacovigilance and identifying novel adverse drug events (ADEs).
Area of Science:
- Pharmacovigilance and Drug Safety
- Medical Informatics
- Natural Language Processing
Background:
- Current pharmacovigilance systems have limitations in fully detecting a drug's safety profile during its market life.
- There is a need for advanced methods to monitor drug safety comprehensively.
Purpose of the Study:
- To demonstrate the feasibility of using natural language processing (NLP), comprehensive Electronic Health Records (EHR), and association statistics for pharmacovigilance.
- To develop a framework for active, high-throughput, and prospective drug safety monitoring.
Main Methods:
- Collected narrative discharge summaries from the Clinical Information System at New York Presbyterian Hospital (NYPH).
- Applied MedLEE, a natural language processing (NLP) system, to identify medication events and potential adverse drug events (ADEs).
- Utilized co-occurrence statistics with adjusted volume tests to detect and analyze associations between drug events and potential ADEs.
Main Results:
- Identified 132 potential adverse drug events (ADEs) associated with 7 selected drugs/drug classes.
- Achieved an overall recall of 0.75 and precision of 0.31 for known ADEs.
- Qualitative evaluation suggested the system's capability to detect novel ADEs.
Conclusions:
- The study presents a feasible framework for enhanced pharmacovigilance using unstructured EHR data.
- This approach offers potential for uncovering comprehensive drug safety profiles throughout a drug's market life.
- Highlights the need for further development to address challenging issues in this novel application of NLP and EHR data.
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