Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Zulfat Miftahutdinov"'
Publikováno v:
Bioinformatics
Motivation Clinical trials are the essential stage of every drug development program for the treatment to become available to patients. Despite the importance of well-structured clinical trial databases and their tremendous value for drug discovery a
Autor:
Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
Publikováno v:
Findings of the Association for Computational Linguistics: ACL 2022.
Autor:
Anne Dirkson, Suzan Verberne, Arjun Magge, Graciela Gonzalez-Hernandez, Elena Tutubalina, Davy Weissenbacher, Ilseyar Alimova, Zulfat Miftahutdinov
Publikováno v:
Journal of the American Medical Informatics Association, 28(10), 2184-2192. Oxford University Press (OUP)
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Objective Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifyi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa36ea5128dda503ed8e5095c7da46ce
http://hdl.handle.net/1887/3246957
http://hdl.handle.net/1887/3246957
Publikováno v:
Scopus-Elsevier
This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extractio
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030721121
ECIR (1)
ECIR (1)
Concept normalization in free-form texts is a crucial step in every text-mining pipeline. Neural architectures based on Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art results in the biomedical domain. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c31e3780ba5f0cfdf71be827bb4e54f8
https://doi.org/10.1007/978-3-030-72113-8_30
https://doi.org/10.1007/978-3-030-72113-8_30
Autor:
Suzan Verberne, Anne Dirkson, Zulfat Miftahutdinov, Graciela Gonzalez-Hernandez, Arjun Magge, Ilseyar Alimova, Elena Tutubalina, Davy Weissenbacher
AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSResearch on pharmacovigilance from social media data has focused on mining adverse drug effects (ADEs) using annotated datasets, with publications generally focusing on one of three tasks: (i) ADE clas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f8022d9dba752c831e183ff92f045ce9
https://doi.org/10.1101/2020.12.15.20248229
https://doi.org/10.1101/2020.12.15.20248229
Autor:
Sergey I. Nikolenko, Elena Tutubalina, Andrey Sakhovskiy, Ilseyar Alimova, Valentin Malykh, Zulfat Miftahutdinov
The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharmaceutical products. The corpus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e115eab028a7ad909af7bd35060985c3
http://arxiv.org/abs/2004.03659
http://arxiv.org/abs/2004.03659
Publikováno v:
COLING
Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation. A large number of biomedical corpora and state-of-the-art models
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030454418
ECIR (2)
ECIR (2)
Although deep neural networks yield state-of-the-art performance in biomedical named entity recognition (bioNER), much research shares one limitation: models are usually trained and evaluated on English texts from a single domain. In this work, we pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c9b530533e73d5135542e6782f90e6e
https://doi.org/10.1007/978-3-030-45442-5_35
https://doi.org/10.1007/978-3-030-45442-5_35
Publikováno v:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task.
This paper describes a system developed for the Social Media Mining for Health (SMM4H) 2019 shared tasks. Specifically, we participated in three tasks. The goals of the first two tasks are to classify whether a tweet contains mentions of adverse drug