A research framework for pharmacovigilance in health social media: Identification and evaluation of patient adverse drug event reports

• Published in: Journal of biomedical informatics 2015-12, Vol.58, p.268-279
• Main Authors: Liu, Xiao, Chen, Hsinchun
• Format: Article
• Language: English
• Subjects: Adverse drug event extraction; Adverse Drug Reaction Reporting Systems; Health social media analytics; Humans; Information search and retrieval; Knowledge acquisition; Pharmacovigilance; Semantics; Social Media; Text mining
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2015.10.011

[Display omitted] •A research framework for patient reported adverse drug event extraction.•Experiments conducted on posts from major diabetes and heart disease forums in US.•Each component significantly contributes to the framework’s overall effectiveness. Social media offer insights of patients’ medical problems such as drug side effects and treatment failures. Patient reports of adverse drug events from social media have great potential to improve current practice of pharmacovigilance. However, extracting patient adverse drug event reports from social media continues to be an important challenge for health informatics research. In this study, we develop a research framework with advanced natural language processing techniques for integrated and high-performance patient reported adverse drug event extraction. The framework consists of medical entity extraction for recognizing patient discussions of drug and events, adverse drug event extraction with shortest dependency path kernel based statistical learning method and semantic filtering with information from medical knowledge bases, and report source classification to tease out noise. To evaluate the proposed framework, a series of experiments were conducted on a test bed encompassing about postings from major diabetes and heart disease forums in the United States. The results reveal that each component of the framework significantly contributes to its overall effectiveness. Our framework significantly outperforms prior work.