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Adverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users

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dc.contributor.authorSboev, A.
dc.contributor.authorRybka, R.
dc.contributor.authorGryaznov, A.
dc.contributor.authorMoloshnikov, I.
dc.contributor.authorSboeva, S.
dc.contributor.authorRylkov, G.
dc.contributor.authorSelivanov, A.
dc.contributor.authorСбоев, Александр Георгиевич
dc.date.accessioned2024-12-25T12:29:14Z
dc.date.available2024-12-25T12:29:14Z
dc.date.issued2022
dc.description.abstractMapping the pharmaceutically significant entities on natural language to standardized terms/concepts is a key task in the development of the systems for pharmacovigilance, marketing, and using drugs out of the application scope. This work estimates the accuracy of mapping adverse reaction mentions to the concepts from the Medical Dictionary of Regulatory Activity (MedDRA) in the case of adverse reactions extracted from the reviews on the use of pharmaceutical products by Russian-speaking Internet users (normalization task). The solution we propose is based on a neural network approach using two neural network models: the first one for encoding concepts, and the second one for encoding mentions. Both models are pre-trained language models, but the second one is additionally tuned for the normalization task using both the Russian Drug Reviews (RDRS) corpus and a set of open English-language corpora automatically translated into Russian. Additional tuning of the model during the proposed procedure increases the accuracy of mentions of adverse drug reactions by 3% on the RDRS corpus. The resulting accuracy for the adverse reaction mentions mapping to the preferred terms of MedDRA in RDRS is 70.9% F1-micro. The paper analyzes the factors that affect the accuracy of solving the task based on a comparison of the RDRS and the CSIRO Adverse Drug Event Corpus (CADEC) corpora. It is shown that the composition of the concepts of the MedDRA and the number of examples for each concept play a key role in the task solution. The proposed model shows a comparable accuracy of 87.5% F1-micro on a subsample of RDRS and CADEC datasets with the same set of MedDRA preferred terms.
dc.identifier.citationAdverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users / Sboev, A. [et al.] // Big Data and Cognitive Computing. - 2022. - 6. - № 4. - 10.3390/bdcc6040145
dc.identifier.doi10.3390/bdcc6040145
dc.identifier.urihttps://www.doi.org/10.3390/bdcc6040145
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85144594217&origin=resultslist
dc.identifier.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=Alerting&SrcApp=Alerting&DestApp=WOS_CPL&DestLinkType=FullRecord&UT=WOS:000900468100001
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/27872
dc.relation.ispartofBig Data and Cognitive Computing
dc.titleAdverse Drug Reaction Concept Normalization in Russian-Language Reviews of Internet Users
dc.typeArticle
dspace.entity.typePublication
oaire.citation.issue4
oaire.citation.volume6
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