A Review on Identification and Evaluation of Patient Adverse Drug Event Report

Social media provides information about patients' health issues, including medication side effects and unsuccessful treatments. Social media patient reports of adverse drug events (ADEs) have the potential to significantly enhance pharmacovigilance procedures as they are today. In health informatics, obtaining these reports is still difficult, though. In this study, we develop a research framework with advanced natural language processing techniques for integrated and high-performance ADE extraction. The framework consists of medical entity extraction, ADE detection using shortest dependency path kernel-based statistical learning, semantic filtering using medical knowledge bases, and report source classification to reduce noise. Experiments were conducted using posts from major U.S.-based diabetes and heart disease forums. The results show each component significantly improves overall effectiveness. Our framework significantly outperforms previous methods.