Papers by Zulfat Miftahutdinov
Fair Evaluation in Concept Normalization: a Large-scale Comparative Analysis for BERT-based Models (2020.coling-main)
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| Challenge: | a large number of biomedical entity mentions are retrieved from different ontologies, requiring non-syntactic interpretation. |
| Approach: | They propose to use bidirectional encoder representations from transformers to link biomedical entities across three domains for a task called medical concept normalization. |
| Outcome: | The proposed neural architectures are efficient for linking biomedical entities across domains and corpora. |
Medical Crossing: a Cross-lingual Evaluation of Clinical Entity Linking (2022.lrec-1)
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Anton Alekseev, Zulfat Miftahutdinov, Elena Tutubalina, Artem Shelmanov, Vladimir Ivanov, Vladimir Kokh, Alexander Nesterov, Manvel Avetisian, Andrei Chertok, Sergey Nikolenko
| Challenge: | Existing approaches to medical entity linking are limited in terms of data volume and languages. |
| Approach: | They propose to use clinical reports, clinical guidelines, and medical research papers to evaluate cross-lingual medical entity linking. |
| Outcome: | The proposed model outperforms existing models on clinical reports, clinical guidelines, and medical research papers. |
A Comprehensive Evaluation of Biomedical Entity-centric Search (2022.emnlp-industry)
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| Challenge: | a novel algorithm for information retrieval from biomedical abstracts is used to identify entities. |
| Approach: | They perform a fine-grained evaluation of a BERT-based biomedical search engine . they use manually annotated PubMed abstracts and off-she-shelf Elasticsearch . |
| Outcome: | The proposed system performs better for disease and gene search queries than other systems. |
Deep Neural Models for Medical Concept Normalization in User-Generated Texts (P19-2)
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| Challenge: | a medical concept normalization problem is a challenge since social media texts are ambiguous and noisy . a recent study shows that neural architectures leverage the semantic meaning of the entity mention . |
| Approach: | They propose to map a health-related entity mention to a controlled vocabulary . they use powerful neural networks and contextualized word representation models . |
| Outcome: | The proposed model outperforms existing state-of-the-art models in mapping medical concepts to medical terms . the proposed model is based on recurrent neural networks and contextualized word representation models . |
RuCCoN: Clinical Concept Normalization in Russian (2022.findings-acl)
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Alexandr Nesterov, Galina Zubkova, Zulfat Miftahutdinov, Vladimir Kokh, Elena Tutubalina, Artem Shelmanov, Anton Alekseev, Manvel Avetisian, Andrey Chertok, Sergey Nikolenko
| Challenge: | a new dataset for clinical concept normalization in Russian is available for download . ehrs contains over 16,028 entity mentions manually linked to over 2,409 unique concepts . |
| Approach: | They present a dataset for clinical concept normalization in Russian manually annotated by medical professionals. |
| Outcome: | The proposed dataset contains 16,028 entity mentions manually linked to over 2,409 unique concepts from the Russian language part of the UMLS ontology. |