Sboev, A.; Selivanov, A.; Moloshnikov, I.; Rybka, R.; Gryaznov, A.; Sboeva, S.; Rylkov, G. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications. Big Data Cogn. Comput.2022, 6, 10.
Sboev, A.; Selivanov, A.; Moloshnikov, I.; Rybka, R.; Gryaznov, A.; Sboeva, S.; Rylkov, G. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications. Big Data Cogn. Comput. 2022, 6, 10.
Sboev, A.; Selivanov, A.; Moloshnikov, I.; Rybka, R.; Gryaznov, A.; Sboeva, S.; Rylkov, G. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications. Big Data Cogn. Comput.2022, 6, 10.
Sboev, A.; Selivanov, A.; Moloshnikov, I.; Rybka, R.; Gryaznov, A.; Sboeva, S.; Rylkov, G. Extraction of the Relations among Significant Pharmacological Entities in Russian-Language Reviews of Internet Users on Medications. Big Data Cogn. Comput. 2022, 6, 10.
Abstract
Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.
Keywords
pharmacological text corpus; automatic relation extraction; natural language processing; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
