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The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tissue Engineering and Regenerative Medicine (TERMeG)es
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorMartinez De Miguel, Claudia
dc.contributor.authorSegura-Bedmar, Isabel
dc.contributor.authorChacon Solano, Esteban Gonzalo
dc.contributor.authorGuerrero Aspizua, Sara
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2023-02-03T14:11:14Z
dc.date.available2023-02-03T14:11:14Z
dc.date.issued2022-01
dc.description.abstractRare diseases affect a small number of people compared to the general population. However, more than 6,000 different rare diseases exist and, in total, they affect more than 300 million people worldwide. Rare diseases share as part of their main problem, the delay in diagnosis and the sparse information available for researchers, clinicians, and patients. Finding a diagnostic can be a very long and frustrating experience for patients and their families. The average diagnostic delay is between 6–8 years. Many of these diseases result in different manifestations among patients, which hampers even more their detection and the correct treatment choice. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments, but most NLP techniques require manually annotated corpora. Therefore, our goal is to create a gold standard corpus annotated with rare diseases and their clinical manifestations. It could be used to train and test NLP approaches and the information extracted through NLP could enrich the knowledge of rare diseases, and thereby, help to reduce the diagnostic delay and improve the treatment of rare diseases. The paper describes the selection of 1,041 texts to be included in the corpus, the annotation process and the annotation guidelines. The entities (disease, rare disease, symptom, sign and anaphor) and the relationships (produces, is a, is acron, is synon, increases risk of, anaphora) were annotated. The RareDis corpus contains more than 5,000 rare diseases and almost 6,000 clinical manifestations are annotated. Moreover, the Inter Annotator Agreement evaluation shows a relatively high agreement (F1-measure equal to 83.5% under exact match criteria for the entities and equal to 81.3% for the relations). Based on these results, this corpus is of high quality, supposing a significant step for the field since there is a scarcity of available corpus annotated with rare diseases. This could open the door to further NLP applications, which would facilitate the diagnosis and treatment of these rare diseases and, therefore, would improve dramatically the quality of life of these patients.en
dc.description.sponsorshipThis work was supported by the Madrid Government (Comunidad de Madrid) under the Multiannual Agreement with UC3M in the line of "Fostering Young Doctors Research" (NLP4RARE-CM-UC3M) and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation; the Multiannual Agreement with UC3M in the line of "Excellence of University Professors (EPUC3M17)"; and a grant from Spanish Ministry of Economy and Competitiveness (SAF2017-86810-R).en
dc.format.extent12
dc.identifier.bibliographicCitationMartínez-de Miguel, C., Segura-Bedmar, I., Chacón-Solano, E. & Guerrero-Aspizua, S. (2022). The RareDis corpus: A corpus annotated with rare diseases, their signs and symptoms. Journal of Biomedical Informatics, 125, 103961.en
dc.identifier.doihttps://doi.org/10.1016/j.jbi.2021.103961
dc.identifier.issn1532-0464
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue103961
dc.identifier.publicationlastpage12
dc.identifier.publicationtitleJournal of Biomedical Informaticsen
dc.identifier.publicationvolume125
dc.identifier.urihttps://hdl.handle.net/10016/36462
dc.identifier.uxxiAR/0000030803
dc.language.isoeng
dc.publisherElsevieren
dc.relation.datasethttps://doi.org/10.21950/DEURZF
dc.relation.projectIDGobierno de España. SAF2017-86810-Res
dc.relation.projectIDComunidad de Madrid. NLP4RARE-CM-UC3Mes
dc.relation.projectIDComunidad de Madrid. EPUC3M17es
dc.rights© 2021 The Author(s).en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaInformáticaes
dc.subject.ecienciaMedicinaes
dc.subject.otherGold-standard corpusen
dc.subject.otherNamed entity recognitionen
dc.subject.otherRelation extractionen
dc.subject.otherRare diseasesen
dc.titleThe RareDis corpus: A corpus annotated with rare diseases, their signs and symptomsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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