Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide

• URL: http://arxiv.org/abs/2504.04346v2
• Date: Tue, 08 Apr 2025 03:11:32 GMT
• Title: Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
• Authors: Zhijie Duan, Kai Wei, Zhaoqian Xue, Jiayan Zhou, Shu Yang, Siyuan Ma, Jin Jin, Lingyao li,
• Abstract summary: Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance.<n>We present a framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG)<n>Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time.
• Score: 8.621699521476371
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy social media content remains a challenging task. We present a systematic framework that leverages large language models (LLMs) to extract medication side effects from social media and organize them into a knowledge graph (KG). We apply this framework to semaglutide for weight loss using data from Reddit. Using the constructed knowledge graph, we perform comprehensive analyses to investigate reported side effects across different semaglutide brands over time. These findings are further validated through comparison with adverse events reported in the FAERS database, providing important patient-centered insights into semaglutide's side effects that complement its safety profile and current knowledge base of semaglutide for both healthcare professionals and patients. Our work demonstrates the feasibility of using LLMs to transform social media data into structured KGs for pharmacovigilance.

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