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An integrated approach for rare disease detection and classification in Spanish pediatric medical reports

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dc.contributor.authorDuque Fernández, Andrés
dc.contributor.authorAraujo Serna, M. Lourdes
dc.contributor.authorMartínez Romo, Juan
dc.contributor.authorEsteban Vasallo, María D.
dc.contributor.authorDomínguez Berjón, María Felicitas
dc.contributor.authorMalillos Pérez, David
dc.contributor.funderAgencia Estatal de Investigación (España)
dc.contributor.funderUniversidad Nacional de Educación a Distancia (UNED)
dc.date.accessioned2026-01-27T08:25:01Z
dc.date.available2026-01-27T08:25:01Z
dc.date.issued2025-10-30
dc.descriptionThe registered version of this article, first published in “Scientific Reports, 15(1), 37973", is available online at the publisher's website: https://doi.org/10.1038/S41598-025-21827-4
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Scientific Reports, 15(1), 37973", está disponible en línea en el sitio web del editor: https://doi.org/10.1038/S41598-025-21827-4
dc.description.abstractRare disease detection and classification is one of the most significant challenges in the application of Natural Language Processing techniques to the analysis and extraction of information from biomedical texts. In this paper, we present a novel research focused on the detection and classification of rare diseases in clinical notes extracted from a cohort of pediatric patients from the Community of Madrid in Spain. From a set of collected and anonymized medical records, we propose a semi-supervised, keyphrase-based system to perform an initial detection of mentions of rare diseases, which is then validated and refined by experts to build a consolidated dataset concerning a subset of different rare diseases. Based on this dataset, we carry out a series of experiments for rare disease classification using both a semi-supervised technique and state-of-the-art supervised systems based on both discriminative and generative models. A detailed case analysis provides insights on which systems excel in specific scenarios and why. The validated dataset contains a total of 1900 annotated texts containing mentions to rare diseases. Experiments on this dataset show that the best supervised models improve the performance of the semi-supervised system by more than 10% (78.74% vs 67.37% micro-average F-Measure), individually enhancing the classification of a significant number of diseases in the dataset. State-of-the-art supervised systems are able to offer promising results on the detection and classification of rare diseases in clinical texts, even in cases for which the amount of annotated information is low. On the other hand, semi-supervised models present interesting capabilities for dealing with limited information and data in the field.en
dc.description.provenanceMade available in DSpace on 2026-01-27T08:25:01Z (GMT). No. of bitstreams: 1 MartinezRomo_Juan_RareDiseases_JUAN MARTÍNEZ ROMO.pdf: 1969042 bytes, checksum: 69c2489e3f6d67a6ebef00bf04bcf39e (MD5) Previous issue date: 2025-10-30en
dc.description.sponsorshipThis work has been partially supported by the Spanish Ministry of Science and Innovation within the OBSER-MENH Project (MCIN/AEI/10.13039 and NextGenerationEU/PRTR) under Grant TED2021-130398B-C21 and EDHER-MED Project under grant PID2022-136522OB-C21, as well as by the Universidad Nacional de Educación a Distancia (UNED) within project SICAMESP (2023-VICE-0029).
dc.description.versionversión publicada
dc.identifier.citationDuque, A., Araujo, L., Martinez-Romo, J., Esteban-Vasallo, M. D., Domínguez-Berjón, M. F., & Malillos Perez, D. (2025). An integrated approach for rare disease detection and classification in Spanish pediatric medical reports. Scientific Reports, 15(1), 37973. DOI https://doi.org/10.1038/s41598-025-21827-4
dc.identifier.doihttps://doi.org/10.1038/S41598-025-21827-4
dc.identifier.issn2045-2322
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31577
dc.journal.issue1
dc.journal.titleScientific Reports
dc.journal.volume15
dc.language.isoes
dc.publisherNature Research
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.relation.projectidinfo:eu-repo/grantAgreement/AEIProyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digital 2021/TED2021-130398B-C21/ES/GELP: Generación mediante procesamiento del lenguaje de perfiles demográficos en redes sociales para la detección de riesgo de suicidio y su relación con otros problemas psicológicos
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Proyectos de I+D+I (Generación de Conocimiento y Retos Investigación) 2022/PID2022-136522OB-C21/ES/Detección precoz de enfermedades de alto impacto mediante el procesamiento del lenguaje natural
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject1203 Ciencia de los ordenadores
dc.subject32 Ciencias Médicas
dc.subject.keywordsRare disease detectionen
dc.subject.keywordsNatural language processingen
dc.subject.keywordsSpanish medical reportsen
dc.subject.keywordsLarge language modelsen
dc.subject.keywordsKeyphrase-based information extractionen
dc.titleAn integrated approach for rare disease detection and classification in Spanish pediatric medical reportsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd6578720-2401-40cf-860c-92822eaf361a
relation.isAuthorOfPublication77c4023e-4374-442a-9dfb-b9d4b609c31e
relation.isAuthorOfPublication91b7e317-2a30-494f-98e9-3a0e026747b1
relation.isAuthorOfPublication.latestForDiscoveryd6578720-2401-40cf-860c-92822eaf361a
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