Contextualized Graph Embeddings for Adverse Drug Event Detection

作者：Ya Gao, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari, Pekka Marttinen · 发表于：Lecture notes in computer science · 年份：2023 · DOI：10.1007/978-3-031-26390-3_35 · 被引用次数：10 · 研究领域：Topic Modeling、Biomedical Text Mining and Ontologies、Pharmacovigilance and Adverse Drug Reactions

Abstract An adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized information and relations among text pieces are less explored. This paper investigates contextualized language models and heterogeneous graph representations. It builds a contextualized graph embedding model for adverse drug event detection. We employ different convolutional graph neural networks and pre-trained contextualized embeddings as the building blocks. Experimental results show that our methods can improve the performance by comparing recent ADE detection models, suggesting that a text graph can capture causal relationships and dependency between different entities in a document.