A Natural Language Processing Framework for Detecting Adverse Drug Reactions in Clinical Structured Drug Reviews

Background: Adverse drug reactions (ADRs) pose significant concerns in healthcare, yet their underreporting remains a challenge. Extracting spontaneous and non-automatic reports from free-text narratives contributes to this low rate of reporting. An automatic ADR detection system can mitigate these issues by identifying, summarizing, and reporting ADRs in a document. This study presents an adverse drug reaction detector (ADRD), a natural language processing (NLP) framework applied to the psychiatric treatment adverse reactions (PsyTAR) dataset. Aiming to automate ADR analysis, the framework explores the relationship between ADRs and patient satisfaction. Methods: A comprehensive eight-phase approach was employed in the ADRD framework, utilizing Python programming language libraries and NLP tools. The dataset underwent meticulous preprocessing, and the subsequent phases involved data summarization, pattern identification, data cleaning, sentiment calculation, assessment of drug effectiveness and usefulness, analysis of medical conditions, and identification of the most effective and ineffective drugs for each condition. Results: Analyzing 891 comments related to four unique drugs (i.e., Zoloft, Lexapro, Cymbalta, and Effexor XR) from patients with 285 distinct conditions, the framework offered insights into the dataset structure, statistical indicators, distribution of ratings and ADR counts, the impact of ratings on ADR counts, and length of comments’ influence on ratings. Conclusion: The challenges of extracting ADR reports from free-text narratives have led to their underreporting. ADRD offers an automated and insightful approach for enhancing ADR analysis and reporting processes, making strides toward bridging the gap in ADR reporting.