Extraction of Adverse Drug Events from Social Media Based on FrameNet Semantic Analysis YOU Liping, WANG Shiyu, LI Chaofan, College of Economics and Management, Shanxi University, Taiyuan 030006, China.

Corresponding author: You Liping, yoliping@163.com

DOI: 10.12201/bmr.202211.00006

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  Abstract: Extract adverse drug events based on social network comment texts to provide reference for drug research and development and safety supervision. The FrameNet semantic theory is adopted, combined with the Medical Dictionary for Regulatory Activities (MedDRA) term set to establish a semantic representation model for adverse drug events; a dictionary and rule-based method is used to identify event categories and frame elements, and semantic information is used to fill the adverse drug event frame. The social network drug evaluation example was selected to extract adverse drug event information, and the feasibility and effectiveness of the method were proved by comparing the extraction results with the original drug instruction manual, which made the framework semantic analysis method deeply applied and realized in the medical professional field.

  Key words: social network; frame semantics; adverse drug events; information extraction

  Submit time: 14 November 2022

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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