Contextualized Graph Embeddings for Adverse Drug Event Detection

Ya Gao, Shaoxiong Ji, Tongxuan Zhang, Prayag Tiwari, Pekka Marttinen

ECML-PKDD 2022 pp. 605-620

doi:10.1007/978-3-031-26390-3_35 /ecmlpkdd/2022/gao2022ecmlpkdd-contextualized/

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 di ﬀ erent 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 di ﬀ erent entities in a document.

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Cite

Text

Gao et al. "Contextualized Graph Embeddings for Adverse Drug Event Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022. doi:10.1007/978-3-031-26390-3_35

Markdown

[Gao et al. "Contextualized Graph Embeddings for Adverse Drug Event Detection." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022.](https://mlanthology.org/ecmlpkdd/2022/gao2022ecmlpkdd-contextualized/) doi:10.1007/978-3-031-26390-3_35

BibTeX

@inproceedings{gao2022ecmlpkdd-contextualized,
  title     = {{Contextualized Graph Embeddings for Adverse Drug Event Detection}},
  author    = {Gao, Ya and Ji, Shaoxiong and Zhang, Tongxuan and Tiwari, Prayag and Marttinen, Pekka},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2022},
  pages     = {605-620},
  doi       = {10.1007/978-3-031-26390-3_35},
  url       = {https://mlanthology.org/ecmlpkdd/2022/gao2022ecmlpkdd-contextualized/}
}