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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">myrwd</journal-id><journal-title-group><journal-title xml:lang="ru">Реальная клиническая практика: данные и доказательства</journal-title><trans-title-group xml:lang="en"><trans-title>Real-World Data &amp; Evidence</trans-title></trans-title-group></journal-title-group><issn pub-type="epub">2782-3784</issn><publisher><publisher-name>Publishing House OKI</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.37489/2782-3784-myrwd-40</article-id><article-id custom-type="edn" pub-id-type="custom">XJXWRQ</article-id><article-id custom-type="elpub" pub-id-type="custom">myrwd-46</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>ARTIFICIAL INTELLIGENCE</subject></subj-group></article-categories><title-group><article-title>Возможности машинного обучения для диагностики орфанных заболеваний</article-title><trans-title-group xml:lang="en"><trans-title>Machine learning capabilities for the diagnosis of orphan diseases</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-1072-243X</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Дмитриева</surname><given-names>Н. Ю.</given-names></name><name name-style="western" xml:lang="en"><surname>Dmitrieva</surname><given-names>N. Y.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Дмитриева Наталия Юрьевна — к. б. н., начальник отдела информационных систем</p><p>Москва</p></bio><bio xml:lang="en"><p>Natalia Y. Dmitrieva — Ph.D. Sc., Head of Information Systems Department</p><p>Moscow</p></bio><email xlink:type="simple">n.dmitrieva@aston-health.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>АО «Астон Консалтинг»</institution><country>Россия</country></aff><aff xml:lang="en"><institution>AO "Aston Consulting"</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2023</year></pub-date><pub-date pub-type="epub"><day>12</day><month>10</month><year>2023</year></pub-date><volume>3</volume><issue>3</issue><fpage>36</fpage><lpage>39</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Дмитриева Н.Ю., 2023</copyright-statement><copyright-year>2023</copyright-year><copyright-holder xml:lang="ru">Дмитриева Н.Ю.</copyright-holder><copyright-holder xml:lang="en">Dmitrieva N.Y.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.myrwd.ru/jour/article/view/46">https://www.myrwd.ru/jour/article/view/46</self-uri><abstract><p>Редкие или орфанные заболевания относятся к одним из наиболее тяжёлых групп заболеваний. При этом ранняя и точная диагностика таких заболеваний является серьёзной проблемой для врачей общей практики, педиатров и терапевтов. В статье рассмотрены возможности применения методов машинного обучения, в том числе искусственного интеллекта, для улучшения диагностики редких болезней. Приводится информация о различных моделях, разработанных, как международными специалистами, так и российскими исследователями.</p></abstract><trans-abstract xml:lang="en"><p>Rare or orphan diseases belong to one of the most severe groups of diseases. At the same time, early and accurate diagnosis of such diseases is a serious problem for general practitioners, pediatricians and therapists. The article discusses the possibilities of using machine learning methods, including artificial intelligence, to improve the diagnosis of rare diseases. Information is provided on various models developed by both international experts and Russian researchers.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>орфанные заболевания</kwd><kwd>машинное обучение</kwd><kwd>редкие болезни</kwd><kwd>клинические регистры</kwd><kwd>наблюдательные программы</kwd><kwd>данные реальной клинической практики</kwd><kwd>RWD</kwd><kwd>RWE</kwd></kwd-group><kwd-group xml:lang="en"><kwd>orphan diseases</kwd><kwd>machine learning</kwd><kwd>rare diseases</kwd><kwd>clinical registries</kwd><kwd>observation programs</kwd><kwd>real-world data</kwd><kwd>RWD</kwd><kwd>RWE</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Segura-Bedmar I, Camino-Perdones D, Guerrero-Aspizua S. Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. 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