Contextualized Graph Embeddings for Adverse Drug Event Detection

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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

2 Sitaatiot (Scopus)
98 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
AlaotsikkoEuropean Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II
ToimittajatMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
KustantajaSpringer
Sivut605–620
Sivumäärä16
ISBN (elektroninen)978-3-031-26390-3
ISBN (painettu)978-3-031-26389-7
DOI - pysyväislinkit
TilaJulkaistu - 17 maalisk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Grenoble, Ranska
Kesto: 19 syysk. 202223 syysk. 2022
https://2022.ecmlpkdd.org/

Julkaisusarja

NimiLecture notes in computer science
Vuosikerta13714
ISSN (painettu)0302-9743
ISSN (elektroninen)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
LyhennettäECML-PKDD
Maa/AlueRanska
KaupunkiGrenoble
Ajanjakso19/09/202223/09/2022
www-osoite

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