kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2026-23-2-171-186kaz29-2896Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEСОЗДАНИЕ КОРПУСА АННОТИРОВАННЫХ МЕДИЦИНСКИХ ТЕКСТОВ ДЛЯ ИЗВЛЕЧЕНИЯ ИНФОРМАЦИИ О ГЕНЕТИЧЕСКИХ ЗАБОЛЕВАНИЯХCONSTRUCTION OF AN ANNOTATED MEDICAL TEXT CORPUS FOR INFORMATION EXTRACTION ON GENETIC DISEASEShttps://orcid.org/0000-0001-9358-1614СамбетбаеваМ. А.SambetbayevaM.

Phd, ассоциированный профессор.

Алматы қ., Астана

PhD, Associate Professor.

Almaty, Astana

sambetbayeva_ma_1@enu.kzhttps://orcid.org/0000-0002-3627-3321СерикбаеваС. К.SerikbaeyvaS.

Phd, и.о.доцента.

Алматы қ., Астана

PhD, Acting Associate Professor.

Almaty, Astana

inf_8585@mail.ruhttps://orcid.org/0009-0009-9038-5234СұлтанғазиеваА. Н.SultangaziyevaA.

Магистр, докторант.

Алматы қ., Астана

Master, PhD student.

Almaty, Astana

anara77777@mail.ruhttps://orcid.org/0000-0003-4835-5751МукажановН. К.MukazhanovN.

PhD, ассоциированный профессор.

Алматы қ.

PhD, Associate Professor.

Almaty

nurzhan.mukazhanov@narxoz.kzhttps://orcid.org/0009-0001-8872-7428АбдығалымБ. Х.Abdy-galymB.

Магистр, докторант.

Алматы қ., Астана

Master, PhD student.

Almaty, Astana

bayangali.abd@gmail.com«Q» University; Евразийский национальный университет имени Л.Н. ГумилеваКазахстанQ university; L.N. Gumilyov Eurasian National UniversityKazakhstan«Q» University; Международный университет АстаныКазахстанQ university; Astana International UniversityKazakhstan«Q» University; Университет НархозКазахстанQ university; Narxoz UniversityKazakhstan202627062026232171186Copyright © Самбетбаева М.А., Серикбаева С.К., Сұлтанғазиева А.Н., Мукажанов Н.К., Абдығалым Б.Х., 20262026Самбетбаева М.А., Серикбаева С.К., Сұлтанғазиева А.Н., Мукажанов Н.К., Абдығалым Б.Х.Sambetbayeva M., Serikbaeyva S., Sultangaziyeva A., Mukazhanov N., Abdy-galym B.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2896

В статье представлен аннотированный корпус, состоящий из клинических текстов на русском языке, полученных по результатам экзомного секвенирования. Данный корпус был разработан в поддержку задач автоматического определения именованных объектов и семантических связей в отношении генов, мутаций, наследственных заболеваний, фенотипических признаков и их клинической значимости. В ходе формирования корпуса были использованы отчеты фактического клинического экзомного секвенирования, данные прошли этапы предварительной анонимизации и нормирования текста. В процессе маркировки использовались международные стандарты и базы знаний, такие как HGVS, OMIM, ClinVar и HPO, а также обеспечивалась согласованность и точность биомедицинской информации. Корпус содержит более 25 000 биомедицинских объектов и более 6000 семантических связей, что делает его важным ресурсом в области клинической генетики с точки зрения объема и содержания. Аннотация проводилась вручную с участием нескольких экспертов, и результаты сравнивались путем перекрестной проверки, а уровень согласия между аннотаторами оценивался с помощью специальных показателей. Полученные результаты свидетельствуют о высоком качестве и надежности корпуса. Готовый корпус позволяет эффективно использовать модели обработки естественного языка в области медицинской генетики для обучения и оценки, разработки систем поддержки принятия клинических решений и прикладных исследований для структурирования генетических данных.

The article presents an annotated corpus consisting of clinical texts in Russian, obtained by exomic sequencing. This corpus was developed to support the tasks of automatically identifying named objects and semantic relationships in relation to genes, mutations, hereditary diseases, phenotypic traits and their clinical significance. During the formation of the corpus, reports of actual clinical exomic sequencing were used, the data went through the stages of preliminary anonymization and text normalization. The labeling process used international standards and knowledge bases such as HGVS, OMIM, ClinVar, and HPO, and ensured consistency and accuracy of biomedical information. The corpus contains more than 25,000 biomedical objects and more than 6,000 semantic links, making it an important resource in the field of clinical genetics in terms of volume and content. The annotation was carried out manually with the participation of several experts, and the results were compared by cross-checking, and the level of agreement between the annotators was assessed using special indicators. The results obtained indicate the high quality and reliability of the case. The finished corpus makes it possible to effectively use natural language processing models in the field of medical genetics for teaching and evaluation, development of clinical decision support systems, and applied research for structuring genetic data.

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The authors declare that there are no conflicts of interest present.