Comparison of text processing methods in social media-based signal detection.
Journal:
Pharmacoepidemiology and drug safety
Published Date:
Aug 7, 2019
Abstract
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine learning (ML). These methods can be categorized by complexity and precision level. Co-occurrence-based ML methods are rather basic, as they identify simultaneous appearance of drugs and clinical events in a single post. In contrast, statistical learning methods involve more complex NLP and identify drugs, events, and associations between them. We aimed to compare the ability of co-occurrence and NLP to identify AEs and signals of disproportionate reporting (SDR) in patient-generated SM. We also examined the performance of lift in SM-based signal detection (SD).
Authors
Keywords
Adverse Drug Reaction Reporting Systems
Data Collection
Datasets as Topic
Drug-Related Side Effects and Adverse Reactions
False Negative Reactions
False Positive Reactions
Feasibility Studies
Natural Language Processing
Pharmacovigilance
Retrospective Studies
Sensitivity and Specificity
Social Media