{"id":10320,"date":"2025-06-25T10:53:00","date_gmt":"2025-06-25T08:53:00","guid":{"rendered":"https:\/\/microchip4age.es\/new-scientific-contribution-from-microchip4age-recognized-by-computer-methods-and-programs-in-biomedicine\/"},"modified":"2025-07-25T11:06:05","modified_gmt":"2025-07-25T09:06:05","slug":"new-scientific-contribution-from-microchip4age-recognized-by-computer-methods-and-programs-in-biomedicine","status":"publish","type":"post","link":"https:\/\/microchip4age.es\/en\/new-scientific-contribution-from-microchip4age-recognized-by-computer-methods-and-programs-in-biomedicine\/","title":{"rendered":"New Scientific Contribution from MicroChip4Age Recognized by Computer Methods and Programs in Biomedicine"},"content":{"rendered":"\n<p><span style=\"font-weight: 400;\">The article introduces an innovative methodology to support people with diabetes\u2014and their caregivers\u2014by adapting language models to the context of disease monitoring.<\/span><\/p>\n\n<p><i><span style=\"font-weight: 400;\">Ja\u00e9n, June 2025<\/span><\/i><span style=\"font-weight: 400;\"> \u2013 The <\/span><b>MicroChip4Age<\/b><span style=\"font-weight: 400;\"> project team has made a decisive step forward in integrating language models with physiological data from diabetic patients. Their study, recently published in the scientific journal <\/span><i><span style=\"font-weight: 400;\">Computer Methods and Programs in Biomedicine<\/span><\/i><span style=\"font-weight: 400;\">, addresses the growing challenges faced by today\u2019s society, particularly those affecting the diabetic community.<\/span><\/p>\n\n<p><span style=\"font-weight: 400;\">Titled <\/span><i><span style=\"font-weight: 400;\">\u201cA novel fine-tuning and evaluation methodology for large language models on IoT raw data summaries (LLM-RawDMeth): A joint perspective in diabetes care\u201d<\/span><\/i><span style=\"font-weight: 400;\">, the paper was developed by researchers <\/span><b>Juan Francisco Gait\u00e1n Guerrero, Carmen Mart\u00ednez Cruz, Macarena Espinilla Est\u00e9vez, David D\u00edaz Jim\u00e9nez<\/b><span style=\"font-weight: 400;\">, and <\/span><b>Jos\u00e9 Luis L\u00f3pez Ruiz<\/b><span style=\"font-weight: 400;\">. The study aims to empower generative models to interpret data from continuous glucose monitoring devices in a way that is accurate, safe, and understandable.<\/span><\/p>\n\n<h3 class=\"wp-block-heading\"><b>Bringing Artificial Intelligence to Clinical Practice<\/b><\/h3>\n\n<p><span style=\"font-weight: 400;\">The study introduces a methodology that combines expert knowledge, fuzzy logic, and advanced training techniques to adapt large language models to the domain of glucose monitoring. Far from simply translating raw data, the system identifies clinically relevant events and expresses them in natural language with a level of detail accessible to both professionals and patients<\/span><\/p>\n\n<p><span style=\"font-weight: 400;\">Additionally, a specialized evaluation framework is included to assess the informative quality, temporal accuracy, and safety of the generated content\u2014aligning the model\u2019s output with the high standards required in medical contexts.<\/span><\/p>\n\n<h3 class=\"wp-block-heading\"><b>A Groundbreaking Approach Based on Real-World Data<\/b><\/h3>\n\n<p><span style=\"font-weight: 400;\">The models were trained on more than 48,000 real glucose measurements and their associated summaries, demonstrating the robustness of the approach in both fully populated and partially available data environments.<\/span><\/p>\n\n<h3 class=\"wp-block-heading\"><b>New Horizons Reached Through MicroChip4Age<\/b><\/h3>\n\n<p><span style=\"font-weight: 400;\">This publication strengthens the methodological foundation of the <\/span><b>MicroChip4Age<\/b><span style=\"font-weight: 400;\"> project, proving that it is possible to build AI-driven solutions that preserve the traceability of clinical knowledge and the interpretability of language\u2014key factors for the real-world adoption of such tools.<\/span><\/p>\n\n<p><b>Link<\/b><span style=\"font-weight: 400;\">:<\/span><a href=\"https:\/\/doi.org\/10.1016\/j.cmpb.2025.108878\"> <span style=\"font-weight: 400;\">doi.org\/10.1016\/j.cmpb.2025.108878<\/span><\/a><\/p>\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The article introduces an innovative methodology to support people with diabetes\u2014and their caregivers\u2014by adapting language models to the context of disease monitoring. Ja\u00e9n, June 2025 \u2013 The MicroChip4Age project team has made a decisive step forward in integrating language models with physiological data from diabetic patients. Their study, recently published in the scientific journal Computer [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":10317,"parent":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[66],"tags":[],"class_list":["post-10320","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-microchip4age-en"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New Scientific Contribution from MicroChip4Age Recognized by Computer Methods and Programs in Biomedicine - Tecnolog\u00eda que protege sin invadir<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/microchip4age.es\/en\/new-scientific-contribution-from-microchip4age-recognized-by-computer-methods-and-programs-in-biomedicine\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New Scientific Contribution from MicroChip4Age Recognized by Computer Methods and Programs in Biomedicine - Tecnolog\u00eda que protege sin invadir\" \/>\n<meta property=\"og:description\" content=\"The article introduces an innovative methodology to support people with diabetes\u2014and their caregivers\u2014by adapting language models to the context of disease monitoring. 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