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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with OASIS Tables with MathML3 v1.4 20241031//EN" "https://jats.nlm.nih.gov/archiving/1.4/JATS-archive-oasis-article1-4-mathml3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" dtd-version="1.4" article-type="research-article" xml:lang="en"><front><journal-meta><journal-title-group><journal-title xml:lang="ru">Математическая физика и компьютерное моделирование</journal-title></journal-title-group><issn publication-format="print">2587-6325</issn><issn publication-format="electronic">2587-6902</issn></journal-meta><article-meta><article-id pub-id-type="doi">10.15688/mpcm.jvolsu.2023.2.3</article-id><article-categories><subj-group><subject>Other</subject></subj-group></article-categories><title-group><article-title xml:lang="ru">Анализ термометрических данных головного мозга, полученных методом микроволновой радиотермометрии</article-title><trans-title-group xml:lang="en"><trans-title>Analysis of brain thermometric data obtained by microwave radiothermometry</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Попов</surname><given-names>Илларион Евгеньевич</given-names></name><name xml:lang="en"><surname>Popov</surname><given-names>Illarion E.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff1"/><email>popov.larion@volsu.ru</email><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0997-8721</contrib-id></contrib><contrib contrib-type="author"><name-alternatives><name xml:lang="ru"><surname>Крылова</surname><given-names>Александра Евгеньевна</given-names></name><name xml:lang="en"><surname>Krylova</surname><given-names>Aleksandra E.</given-names></name></name-alternatives><xref ref-type="aff" rid="aff1"/><email>a.krylova@volsu.ru</email><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-0997-8721</contrib-id></contrib><aff-alternatives id="aff1"><aff xml:lang="en"><institution>Volgograd State University(Volgograd, Russian Federation)</institution></aff><aff xml:lang="ru"><institution>Волгоградский государственный университет(г. Волгоград, Российская Федерация)</institution></aff></aff-alternatives></contrib-group><pub-date pub-type="epub" iso-8601-date="2023-05-20"><day>20</day><month>05</month><year>2023</year></pub-date><volume>26</volume><issue>2</issue><fpage>32</fpage><lpage>42</lpage><history><date date-type="received" iso-8601-date="2023-02-27"><day>27</day><month>02</month><year>2023</year></date><date date-type="accepted" iso-8601-date="2023-04-26"><day>26</day><month>04</month><year>2023</year></date></history><permissions><license xlink:href="https://creativecommons.org/licenses/by-nc/4.0/" xlink:title="CC BY-NC 4.0"><ali:license_ref>https://creativecommons.org/licenses/by-nc/4.0/</ali:license_ref><license-p xml:lang="ru">CC BY-NC 4.0</license-p></license></permissions><abstract xml:lang="ru"><p>Работа выполнена в рамках направления, основной целью которого является разработка моделей, описывающих диагностическое состояние пациентов. Модели строятся на основе известных знаний медицины и анализа данных измерений, полученных методом микроволновой радиотермометрии. В работе предложена концептуальная модель, описывающая характеристические особенности температурных полей здоровых пациентов на основе уровня выраженности циркадного ритма. По данным особенностям была построена математическая модель, элементы которой характеризуют положения концептуальной модели. Были проведены вычислительные эксперименты, в которых определялась эффективность алгоритма классификации, обучаемого по данным математической модели. В результате было показано, что методРабота выполнена в рамках направления, основной целью которого является разработка моделей, описывающих диагностическое состояние пациентов. Модели строятся на основе известных знаний медицины и анализа данных измерений, полученных методом микроволновой радиотермометрии. В работе предложена концептуальная модель, описывающая характеристические особенности температурных полей здоровых пациентов на основе уровня выраженности циркадного ритма. По данным особенностям была построена математическая модель, элементы которой характеризуют положения концептуальной модели. Были проведены вычислительные эксперименты, в которых определялась эффективность алгоритма классификации, обучаемого по данным математической модели. В результате было показано, что метод микроволновой радиотермометрии эффективен в выявлении пациентов с нарушенным сознанием.</p></abstract><abstract xml:lang="en" abstract-type="summary"><p>This paper discusses the effectiveness of using the method of microwave radiothermometry in examinations of brain diseases, namely the state of disordered consciousness. In contrast to most methods of examinations by this method, the measurements of the brain were carried out in only 2 different frontal areas for 2 days with a frequency of 4 hours. Therefore, the aim of the study was to identify the effectiveness of a diagnostic model based on the dynamics of temperature changes. The work showed that in healthy patients there is a circadian rhythm: during the day the temperature rises, at night it decreases. At the same time, such dynamics is not observed in patients with disordered consciousness. Based on this knowledge, a conceptual and mathematical model were proposed. The first of them describes the characteristic features of healthy and sick patients. The second one quantifies these features. The constructed mathematical model was tested in the classification problem. The Naive Bayes classifier was used as a classifier. As a result of computational experiments, it was shown that for 500 iterations the classifier made a mistake on only 1 sick patient and 5 healthy ones. Thus, the effectiveness of the method of microwave radiothermometry in the task of examining patients with disordered consciousness was shown.</p></abstract><kwd-group xml:lang="ru"><kwd>микроволновая радиотермометрия</kwd><kwd>машинное обучение</kwd><kwd>интеллектуальный анализ данных</kwd><kwd>циркадный ритм</kwd></kwd-group><kwd-group xml:lang="en"><kwd>microwave radiothermometry</kwd><kwd>machine learning</kwd><kwd>data mining</kwd><kwd>circadian rhythm</kwd><kwd>classification algorithm</kwd></kwd-group></article-meta></front><back><ref-list><ref id="ref1"><mixed-citation publication-type="other" xml:lang="ru">Диагностические возможности неинвазивного термомониторинга головного мозга / Д. В. Чебоксаров, А. В. Бутров, О. А. Шевелев, В. Г. Амчеславский, Н. Н. Пулина, М. А. Буитина, И. М. Соколов // Анестезиология и реаниматология. — 2015. — Т. 60, №1. — C. 66–69.</mixed-citation></ref><ref id="ref2"><mixed-citation publication-type="other" xml:lang="ru">Замечник, Т. В. 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