Contextualized Graph Embeddings for Adverse Drug Event Detection
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A4 Artikkeli konferenssijulkaisussa
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Date
2023-03-17
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en
Pages
16
605–620
605–620
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
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.Description
| openaire: EC/H2020/101016775/EU//INTERVENE
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Gao, Y, Ji, S, Zhang, T, Tiwari, P & Marttinen, P 2023, Contextualized Graph Embeddings for Adverse Drug Event Detection . in M-R Amini, S Canu, A Fischer, T Guns, P Kralj Novak & G Tsoumakas (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings : European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II . Lecture notes in computer science, vol. 13714, Springer, pp. 605–620, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Grenoble, France, 19/09/2022 . https://doi.org/10.1007/978-3-031-26390-3_35