RuMedBench: A Russian Medical Language Understanding Benchmark

Blinov, Pavel; Reshetnikova, Arina; Нестеров, А. Г.; Zubkova, Galina; Kokh, Vladimir

doi:10.1007/978-3-031-09342-5_38

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Today, there are a large number of deep-learning models used for processing medical texts. The largest number of such models work in the English-speaking field of text analysis [14,15]. However, some models have been pre-trained in Russian.…”

Section: Applying Pre-trained Modelsmentioning

confidence: 99%

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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The purpose of this paper is to study approaches to the intellectual processing of Russianlanguage textual medical information concerning the thyroid cytopathology description to solve the issues of their classification and generation of the text of the medical report, as well as augmentation of descriptions in their acute shortage. Over the past decade, the field of biomedicine has not undergone significant changes. Approaches to analyzing patients' problems are mostly based on manual processing and expert knowledge of doctors. The paper considers the creation of a machine-learning pipeline containing a full cycle of data preprocessing and model training in the field of thyroid nodules fine-needle aspiration classification according to the Bethesda thyroid cytopathology reporting system. Sequential and transformer neural networks were used to design the architecture of deep learning models. The paper proposes approaches for cleaning and preprocessing raw medical descriptions to the required type. The obtained results show that sequential neural networks have greater accuracy on small data sets, and transformation architectures are superior to others when generating cytopathological reports on large amounts of data. The solution obtained in the study can be used as an additional reference tool for thyroid cytologists.

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Section: Applying Pre-trained Modelsmentioning

confidence: 99%

Using Deep Learning to Generate and Classify Thyroid Cytopathology Reports According to The Bethesda System

Diuldin,

Makanov,

Shifman

et al. 2024

RIA

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“…Currently, there are several annotated corpora for the extraction of diseases, drugs, and adverse drug reactions from social media and clinical records in Russian ( Tutubalina et al 2021 ; Nesterov et al 2022 ). A recent work on a Russian medical language understanding benchmark ( Blinov et al 2022 ) includes the RuDReC corpus ( Tutubalina et al 2021 ) for named entity recognition (NER). However, these corpora do not cover scientific texts and include flat (non-nesting) entity mentions only.…”

Section: Introductionmentioning

confidence: 99%

NEREL-BIO: a dataset of biomedical abstracts annotated with nested named entities

et al. 2023

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Motivation This paper describes NEREL-BIO – an annotation scheme and corpus of PubMed abstracts in Russian and smaller number of abstracts in English. NEREL-BIO extends the general domain dataset NEREL (Loukachevitch et al., 2021) by introducing domain-specific entity types. NEREL-BIO annotation scheme covers both general and biomedical domains making it suitable for domain transfer experiments. NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL. Nested named entities may cross entity boundaries to connect to shorter entities nested within longer entities, making them harder to detect. Results NEREL-BIO contains annotations for 700+ Russian and 100+ English abstracts. All English PubMed annotations have corresponding Russian counterparts. Thus, NEREL-BIO comprises the following specific features: annotation of nested named entities, it can be used as a benchmark for cross-domain (NEREL → NEREL-BIO) and cross-language (English → Russian) transfer. We experiment with both transformer-based sequence models and machine reading comprehension (MRC) models and report their results. Availability The dataset and annotation guidelines are freely available at https://github.com/nerel-ds/NEREL-BIO.

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