Medical terminology of patients’ medical cases: structural and semantic analysis

Cover Page

Cite item

Full Text

Abstract

The research focuses on the pivotal issue of human-related nominations of medical terminology. The scrutiny is given to the terminology of patients’ medical cases and complete management. The work attempts to fill in the research niche of structural, semantic analysis and specifics of interlinguistic equivalents of English and Russian medical terminology of patient cases. The nominations of human body, conditions, triggers and medical manipulations are studied. The research material, followed methods and approaches contribute to the research relevance implemented within human-oriented paradigm. The study aims at clarifying and specifying the semantic and structural features of medical terms elicited from the subcorpora of patient cases presented in the ‘House M.D.’ TV series, compiled by the authors. The aim is achieved through the study of semantic components, contextual features, lexical valency and derivational features of 168 terms. The corpora-based approach combined with text proces sing tools and techniques used by the authors make the research novel. Moreover, the developed algorithm provides a solid base for further investigations alike. The research was implemented in four stages. The word frequency analysis of the words in the compiled corpora showed the prevalence of medical terms. The number of terminological word combinations equals to one-word terms. Among the former word combinations with prepositions are not frequent. The latter exhibit clear derivational patterns with a marked set of word formation suffixes. The distribution of the terms into semantic groups revealed the prevalence of the semes ‘diagnosis’, ‘symptoms’ and ‘pathology’. The need for transliteration of English-Russian equivalents of medical terms arises due to the Latin origin of the most part of the terms which may be regarded as international medical vocabulary.

Full Text

Introduction

There has been a marked interest towards the study of medical terminology recently [Andreeva, Amatych 2024; Shaekhova, Andreeva 2024; Suyunov 2024; Gorbunova, Makarova, Kazakova 2023; Akhmedov 2020; Bilyalova, Bazarova 2017; Fiordo 2012]. This research work studies medical terms (hereinafter MTs) elicited from the patient medical cases presented in the ‘House M.D.’ TV series.

Traditionally, terminological units are classified according to the nature of their availability in dictionaries [Andreeva, Amatych 2024; Solnyshkina, Kalinkina, Ziganshina 2015]. Terminological units found in paper and electronic dictionaries are defined as codified and thus are viewed as a core of professional terminology. The terms are coined and generated by speakers of any professional or social group and may lack dictionary definitions. These terms are classified as uncodified peripheral ones [Solnyshkina, Kalinkina, Ziganshina 2015]. The MTs researched in given study are codified units used by both medical and nonmedical professionals globally.

As one of the major anthropocentric disciplines medicine is of primary importance for every person. The language units used by medical professionals more often than not require thorough understanding. Current work sheds the light on the semantic, lexical structural? and translation features of the MTs viewed as a research niche. It explains the relevance of the research. The research novelty lies in the combination of methods used to study MTs, in particular, semantic, lexicographic, distributional, contextual and translation analysis and offline text processing tools. To the best of our knowledge such an algorithm has not been implemented yet.

The research aims at determining lexical, semantic and contextual features of MTs used in patient medical cases management.

The stated aim predetermines the research tasks: 1) to compile a subcorpus of medical terminology elicited from ‘House M.D.’ TV series of Episodes 1 – 5 of Season 1 (hereinafter Subcorpus 1); 2) to compile a subcorpus of short plots of five episodes of ‘House, M.D.’ Season 1 (hereinafter Subcorpus 2); 3) to perform a part-of-speech classification of one-word MTs and determine structural types of word combinations; 4) to group one-word MTs based on the common suffixes; 5) to perform semantic analysis, categorizing the MTs into topic groups; 6) to categorize the MTs into groups according to their English-Russian equivalents.

The analysis of medical records of the patients who deal with thoracic issues revealed the prevalence of anatomical terminology pertaining to various human body parts [Andreeva, Amatych 2024]. Authors also resort to the historical analysis to reveal the etymology of medical terminology elicited from the academic sources and determine the differences of the terms meaning through the years [Suyunov 2024]. Akhmedov (2020) highlight the significance of all parts of speech to be involved in the analysis medical terminology and develop the multidisciplinary approach to allot semantic and lexical issues in clinical practice [Akhmedov 2020]. The study of lexical and semantic features of medical terms of human diseases exhibit no violations of meaning distinctness of the terms and synonyms, antonyms, polysemy and homonymy processes among medical terms are different from those of general vocabulary [Bilyalova, Bazarova, Gilyazeva, 2017].

The research was implemented in four stages aimed at elucidating the following research questions (RQ):

RQ1: What are the most frequent MTs and their collocations revealed in the patients’ cases?

RQ2: Are MTs, predominantly, one-word units or word combinations?

RQ3: What topic groups do the MTs constitute?

RQ4: What is the translation specifics English MTs and their Russian equivalents?

Material and methods

The research implied the analysis of 168 MTs elicited from the TV series ‘House M.D.’ (HMD) Season 1, Episodes 1 – 5. The research is based on two subcorpora. The complete utterances containing MTs were registered in the authors’ Subcorpus 1 and coded. The code contains numbers and letters, for example, code E1.A10 marks Episode 1, Anatomy, example sentence 10.

Besides, the authors compiled Subcorpus 2 of short plot descriptions, comprising 1304 tokens, extracted from open online resources (WHMD). The first stage of the research implied the use of offline text analyzer AntConc (AntConc). The tool processes any input text in txt file and generates N-Grams, KWIC search results, concordances, and word clouds (AntConc).

The research rested on the following methods: continuous sampling, description, comparison and contrasting, lexicographic analysis, contextual analysis.

The second stage of the research rested on the description and classification of word combinations as structural entities. According to V.D. Arakin (2005), the main component of the structure is termed kernel (or core (hereinafter K)), whereas the peripheral one is an adjunct (hereinafter A) [Arakin 2005]. The kernel and adjunct may be manifested by words of various parts of speech, namely, verbs (v), nouns (n), adjectives (a), adverbs (d) and prepositions (prep).

To perform research stage three we need to take a closer look at the structure of the word meaning. Obviously, it is a multi-layer phenomenon that comprises deep and superficial structures, called semes or ‘minimal components of meaning’ [Naciscione 2010]. Deep structures are termed as a core and superficial ones are viewed as a periphery [Sternin, Salomatina 2011]. Core seme of the word doctor is ‘a person’, peripheral ones may include ‘medicine’, ‘male’, ‘female’, ‘treat’, ‘health’ and ‘issues’. To obtain the semes we need to resort to the dictionaries’ definitions and context. The latter is of primary importance as it is an ample source of semantic and lexical valency of a word [Kupriyanov, Solnyshkina, Dascalu, Soldatkina 2022; Kazakova, Gorbunova, 2024; Karachina 2023].

Results and Discussion

At Stage I the cases of the patients in each of the five series were briefly described. The description was obtained from a short plot presentation provided by online sources (WHMD). Thus we compiled a study Subcorpus 2 of 1304 tokens that was further processed with the AntConc offline tool (AntConc) (see Fig. 1).

The most frequent words apart from the articles, prepositions and pronouns were patient (9)[1], diagnosis (7), blood (7), prescribe (7), symptoms (5), clinic (4), hospital (4), evidence (4), infection (3). Clearly, the vast majority of the most frequent content words were medicine-related ones, as their semantic structure met the inclusion criteria (see Stage III further).

The AntConc tool also provides KWIC word collocations outline (see Fig. 2).

 

[1] Hereinafter the numbers in brackets indicate the frequency of the MT in given contexts.

×

About the authors

M. I. Andreeva

Kazan State Medical University

Author for correspondence.
Email: maria.andreeva@kazangmu.ru

Candidate of Philological Sciences, associate professor of the Department of Foreign Languages

Russian Federation, 49, Butlerov Street, Kazan, 420012, Russian Federation

R. R. Shaekhova

Kazan State Medical University

Email: shaehova.regina@yandex.ru

student of the Faculty of General Medicine

Russian Federation, 49, Butlerov Street, Kazan, 420012, Russian Federation

References

  1. Akhmedov, Bobojonova 2020 – Akhmedov O.S., Bobojonova Sh.Y.Q. (2020) Semantic analysis of medical lexicon in united medical language system. Herald of Science and Education, no. 15–2 (93), pp. 39–41. Available at: https://www.elibrary.ru/item.asp?id=43771264. EDN: https://www.elibrary.ru/item.asp?id=43771264.
  2. Andreeva, Amatych 2024 – Andreeva M.I., Amatych V.A. (2024) Medical terminology of case records: lexis, semantics and context. The world of science, culture and education, no. 2 (105), pp. 369–371. DOI: http://doi.org/10.24412/1991-5497-2024-2105-369-371.
  3. Bikbulatova 2023 – Bikbulatova Z.F. (2023) The translation specifics of dental fact sheets. In: X international youth scientific medical forum «White Flowers» dedicated to the 150th anniversary of S.S. Zimnitsky. Kazan: Kazanskii gosudarstvennyi meditsinskii universitet, pp. 312–313. Available at: https://www.elibrary.ru/item.asp?id=54700700. EDN: https://www.elibrary.ru/qmwmmy.
  4. Bilyalova, Bazarova, Gilyazeva 2017 – Bilyalova A.A., Bazarova L.V., Gilyazeva E.N. (2017) Types Of Semantic Relations In The Medical Terminology of The English And Russian Languages. Modern Journal of Language Teaching Methods (MJLTM), vol. 7, issue 9.1, pp. 105–110. Available at: https://kpfu.ru/staff_files/F2083486540/Bilyalova_A.A.__Bazarova_L.V__Gilyazeva_E.N.___Types_of_semantic_relations_in_the_medical_terminology.pdf.
  5. Fiordo 2012 – Fiordo R. (2012) General semantics, science, and medicine: A quality approach. ETC: A Review of General Semantics, vol. 69, no. 4, pp. 364–381. Available at: https://www.jstor.org/stable/42579210.
  6. Gorbunova, Makarova, Kazakova 2023 – Gorbunova D.V., Makarova O.Y., Kazakova U.A. (2023) Professional tolerance of a physician within the professional medical culture. Mir Nauki, Kultury, Obrazovaniya, no. 6 (103), pp. 336–338. DOI: http://doi.org/10.24412/1991-5497-2023-6103-336-338. Available at: https://www.elibrary.ru/item.asp?id=59756136. EDN: https://www.elibrary.ru/czrhen.
  7. Kazakova, Gorbunova, 2024 – Kazakova A.V., Gorbunova D.V. (2024) Neologims in the medical terminology. In: Traditions and innovations in teaching foreign languages: proceedings of the XIII All-Russian research and practical conference with international participation. Kazan, pp. 183–191. Available at: https://www.elibrary.ru/item.asp?id=67956788. EDN: https://www.elibrary.ru/jvqzpk.
  8. Kupriyanov, Solnyshkina, Dascalu, Soldatkina 2022 – Kupriyanov R.V., Solnyshkina M.I., Dascalu M., Soldatkina T.A. (2022) Lexical and syntactic features of academic Russian texts: a discriminant analysis. Research Result. Theoretical and Applied Linguistics, vol. 8, no. 4, pp. 105–122. DOI: http://doi.org/10.18413/2313-8912-2022-8-4-0-8. Available at: https://www.elibrary.ru/item.asp?id=50060471. EDN: https://www.elibrary.ru/knaltd.
  9. Languages – Languages. Latin: grammar, morphology and syntax, word formation. Retrieved from the official website of the electronic library «Artefact». Available at: http://artefact.lib.ru/languages/lat_ebooks_sob1_word_building.shtml (accessed 21.07.2024)
  10. Naciscione 2010 – Naciscione A. (2010) Stylistic use of phraseological units in discourse. Amsterdam & Philadelphia: John Benjamins Publishing Company, 292 p. DOI: http://doi.org/10.1075/z.159. Available at: https://library.oapen.org/viewer/web/viewer.html?file=/bitstream/handle/20.500.12657/31773/625262.pdf?sequence=1&isAllowed=y.
  11. Shaekhova, Andreeva 2024 – Shaekhova R.R., Andreeva M.I. (2024) Medical terms: context, meaning, structure. Na peresechenii yazykov i kul'tur. Aktual'nye voprosy gumanitarnogo znaniya, no. 1 (28), pp. 138–141. Available at: https://www.elibrary.ru/item.asp?id=67315617. EDN: https://www.elibrary.ru/vjvovc.
  12. Suyunov 2024 – Suyunov B.T. (2024) Development of meaning in words and semantics of medical terms. European Science Methodical Journal, vol. 2, no. 6, pp. 187–193. Available at: https://europeanscience.org/index.php/3/article/view/702/677.
  13. Arakin 2005 – Arakin V.D. (2005) Comparative typology of English and Russian languages. Moscow: Fizmatlit, 232 p. Available at: http://library.lgaki.info:404/2020 = Аракин_Сравнительная_типология.pdf. (In Russ.) Аракин 2005 – Аракин В.Д. Сравнительная типология английского и русского языков. Москва: Физматлит, 2005. 232 с. URL: http://library.lgaki.info:404/2020/Аракин_Сравнительная_типология.pdf.
  14. Karachina 2023 – Karachina T.I. (2023) Wordformation in English medical terminology for symptom describing. In: Traditions and innovations in teaching foreign languages: Proceedings of the XII All-Russian research and practical conference, Kazan, June 02, 2023. Kazan: Kazanskii gosudarstvennyi meditsinskii universitet, pp. 120–128. Available at: https://www.elibrary.ru/item.asp?id=54071684. EDN: https://www.elibrary.ru/zwftbn. (In Russ.) = Карачина 2023 – Карачина Т.И. Словообразование в медицинской терминологии на английском языке для описания симптомов заболевания // Традиции и инновации в преподавании иностранного языка: материалы XII Всероссийской научно-практической конференции, Казань, 02 июня 2023 года. Казань: Казанский государственный медицинский университет, 2023. С. 120–128. URL: https://www.elibrary.ru/item.asp?id=54071684. EDN: https://www.elibrary.ru/zwftbn.
  15. Solnyshkina, Kalinkina, Ziganshina 2015 – Solnyshkina M.I., Kalinkina T.E., Ziganshina Ch.R. (2015) Conventions of professional communication. Philology and Culture, no. 3 (41), pp. 138–145. Available at: https://www.elibrary.ru/item.asp?id=24038511. EDN: https://www.elibrary.ru/ugaxhd. (In Russ.) = Солнышкина, Калинкина, Зиганшина 2015 – Солнышкина М.И., Калинкина Т.Е., Зиганшина Ч.Р. Конвенции профессиональной коммуникации // Филология и культура. 2015. № 3 (41). С. 138–145. URL: https://www.elibrary.ru/item.asp?id=24038511. EDN: https://www.elibrary.ru/ugaxhd.
  16. Sternin, Salomatina 2011 – Sternin I.A., Salomatina M.A. (2011) Semantic analysis of a word in a context. Voronezh: Istoki, 150 p. Available at: http://sterninia.ru/files/757/4_Izbrannye_nauchnye_publikacii/Semasiologija/Semanticheskij_analiz_slova_v_kontekste.pdf; https://www.elibrary.ru/item.asp?id=25560566. EDN: https://www.elibrary.ru/vniwwx. (In Russ.) = Стернин, Саломатина 2011 – Стернин И.А., Саломатина М.А. (2011) Семантический анализ слова в контексте. Воронеж: Истоки, 2011, 150 c. URL: http://sterninia.ru/files/757/4_Izbrannye_nauchnye_publikacii/Semasiologija/Semanticheskij_analiz_slova_v_kontekste.pdf; https://www.elibrary.ru/item.asp?id=25560566. EDN: https://www.elibrary.ru/vniwwx.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2025 Andreeva M.I., Shaekhova R.R.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Согласие на обработку персональных данных с помощью сервиса «Яндекс.Метрика»

1. Я (далее – «Пользователь» или «Субъект персональных данных»), осуществляя использование сайта https://journals.rcsi.science/ (далее – «Сайт»), подтверждая свою полную дееспособность даю согласие на обработку персональных данных с использованием средств автоматизации Оператору - федеральному государственному бюджетному учреждению «Российский центр научной информации» (РЦНИ), далее – «Оператор», расположенному по адресу: 119991, г. Москва, Ленинский просп., д.32А, со следующими условиями.

2. Категории обрабатываемых данных: файлы «cookies» (куки-файлы). Файлы «cookie» – это небольшой текстовый файл, который веб-сервер может хранить в браузере Пользователя. Данные файлы веб-сервер загружает на устройство Пользователя при посещении им Сайта. При каждом следующем посещении Пользователем Сайта «cookie» файлы отправляются на Сайт Оператора. Данные файлы позволяют Сайту распознавать устройство Пользователя. Содержимое такого файла может как относиться, так и не относиться к персональным данным, в зависимости от того, содержит ли такой файл персональные данные или содержит обезличенные технические данные.

3. Цель обработки персональных данных: анализ пользовательской активности с помощью сервиса «Яндекс.Метрика».

4. Категории субъектов персональных данных: все Пользователи Сайта, которые дали согласие на обработку файлов «cookie».

5. Способы обработки: сбор, запись, систематизация, накопление, хранение, уточнение (обновление, изменение), извлечение, использование, передача (доступ, предоставление), блокирование, удаление, уничтожение персональных данных.

6. Срок обработки и хранения: до получения от Субъекта персональных данных требования о прекращении обработки/отзыва согласия.

7. Способ отзыва: заявление об отзыве в письменном виде путём его направления на адрес электронной почты Оператора: info@rcsi.science или путем письменного обращения по юридическому адресу: 119991, г. Москва, Ленинский просп., д.32А

8. Субъект персональных данных вправе запретить своему оборудованию прием этих данных или ограничить прием этих данных. При отказе от получения таких данных или при ограничении приема данных некоторые функции Сайта могут работать некорректно. Субъект персональных данных обязуется сам настроить свое оборудование таким способом, чтобы оно обеспечивало адекватный его желаниям режим работы и уровень защиты данных файлов «cookie», Оператор не предоставляет технологических и правовых консультаций на темы подобного характера.

9. Порядок уничтожения персональных данных при достижении цели их обработки или при наступлении иных законных оснований определяется Оператором в соответствии с законодательством Российской Федерации.

10. Я согласен/согласна квалифицировать в качестве своей простой электронной подписи под настоящим Согласием и под Политикой обработки персональных данных выполнение мною следующего действия на сайте: https://journals.rcsi.science/ нажатие мною на интерфейсе с текстом: «Сайт использует сервис «Яндекс.Метрика» (который использует файлы «cookie») на элемент с текстом «Принять и продолжить».