Multimodal model with text and drug embeddings for adverse drug reaction classification.
In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text and drug structure representations are complementary, we introduce a multimodal model with two components. These compone...
| Publicado en: | Journal of Biomedical Informatics Vol. 135 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Nov2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160174327&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160174327 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Nov2022 vid: 135 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 160174327 160174327 NLM36184069 160174327 10.1016/j.jbi.2022.104182 NLM36184069 160174327 ppct: 1 formats: tig: atl: Multimodal model with text and drug embeddings for adverse drug reaction classification. aug: au: Sakhovskiy, Andrey Tutubalina, Elena affil: Kazan Federal University, 18 Kremlyovskaya street, Kazan, 420008, Russian Federation sug: subj: Adverse Drug Event Social Media Data Mining Methods Natural Language Processing Clinical Assessment Tools Social Readjustment Rating Scale ab: In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text and drug structure representations are complementary, we introduce a multimodal model with two components. These components are state-of-the-art BERT-based models for language understanding and molecular property prediction. Experiments were carried out on multilingual benchmarks of the Social Media Mining for Health Research and Applications (#SMM4H) initiative. Our models obtained state-of-the-art results of 0.61 F1-measure and 0.57 F1-measure on #SMM4H 2021 Shared Tasks 1a and 2 in English and Russian, respectively. On the classification of French tweets from SMM4H 2020 Task 1, our approach pushes the state of the art by an absolute gain of 8% F1. Our experiments show that the molecular information obtained from neural networks is more beneficial for ADE classification than traditional molecular descriptors. The source code for our models is freely available at https://github.com/Andoree/smm4h_2021_classification. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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