no code implementations • Findings (ACL) 2022 • Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
We present RuCCoN, a new dataset for clinical concept normalization in Russian manually annotated by medical professionals.
no code implementations • SMM4H (COLING) 2020 • Ari Klein, Ilseyar Alimova, Ivan Flores, Arjun Magge, Zulfat Miftahutdinov, Anne-Lyse Minard, Karen O’Connor, Abeed Sarker, Elena Tutubalina, Davy Weissenbacher, Graciela Gonzalez-Hernandez
The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics.
1 code implementation • SMM4H (COLING) 2020 • Zulfat Miftahutdinov, Andrey Sakhovskiy, Elena Tutubalina
The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58% on English tweets and 51% on Russian tweets.
no code implementations • NAACL (SMM4H) 2021 • Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-Garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre, Salvador Lima López, Ivan Flores, Karen O’Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
The global growth of social media usage over the past decade has opened research avenues for mining health related information that can ultimately be used to improve public health.
1 code implementation • LREC 2022 • Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
Medical data annotation requires highly qualified expertise.
no code implementations • NAACL (SMM4H) 2021 • Andrey Sakhovskiy, Zulfat Miftahutdinov, Elena Tutubalina
This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task.
1 code implementation • 21 Nov 2023 • Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, Alán Aspuru-Guzik, Alex Zhavoronkov
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions.
1 code implementation • 22 Jan 2021 • Zulfat Miftahutdinov, Artur Kadurin, Roman Kudrin, Elena Tutubalina
We investigate the effectiveness of transferring concept normalization from the general biomedical domain to the clinical trials domain in a zero-shot setting with an absence of labeled data.
1 code implementation • COLING 2020 • Elena Tutubalina, Artur Kadurin, Zulfat Miftahutdinov
Linking of biomedical entity mentions to various terminologies of chemicals, diseases, genes, adverse drug reactions is a challenging task, often requiring non-syntactic interpretation.
1 code implementation • 7 Apr 2020 • Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov, Andrey Sakhovskiy, Valentin Malykh, Sergey Nikolenko
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
no code implementations • 16 Aug 2019 • Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov, Eugene Beloded
We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.
no code implementations • WS 2019 • Zulfat Miftahutdinov, Ilseyar Alimova, Elena Tutubalina
The end-to-end model based on BERT for ADR normalization ranked first at the SMM4H 2019 Task 3 and obtained a relaxed F1 of 43. 2{\%}.
no code implementations • ACL 2019 • Zulfat Miftahutdinov, Elena Tutubalina
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a health-related entity mention in a free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
no code implementations • 28 Nov 2018 • Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
no code implementations • 4 Dec 2017 • Elena Tutubalina, Zulfat Miftahutdinov
Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest.