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Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts

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dc.affiliation.dptoUC3M. Departamento de Informáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Human Language and Accessibility Technologies (HULAT)es
dc.contributor.authorSegura Bedmar, Isabel
dc.contributor.authorCamino Perdones, David
dc.contributor.authorGuerrero Aspizua, Sara
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia e Innovación (España)es
dc.date.accessioned2023-12-20T15:50:11Z
dc.date.available2023-12-20T15:50:11Z
dc.date.issued2022-07-06
dc.description.abstractBackground and objective: Although rare diseases are characterized by low prevalence, approximately 400 million people are affected by a rare disease. The early and accurate diagnosis of these conditions is a major challenge for general practitioners, who do not have enough knowledge to identify them. In addition to this, rare diseases usually show a wide variety of manifestations, which might make the diagnosis even more difficult. A delayed diagnosis can negatively affect the patient"s life. Therefore, there is an urgent need to increase the scientific and medical knowledge about rare diseases. Natural Language Processing (NLP) and Deep Learning can help to extract relevant information about rare diseases to facilitate their diagnosis and treatments. Methods: The paper explores several deep learning techniques such as Bidirectional Long Short Term Memory (BiLSTM) networks or deep contextualized word representations based on Bidirectional Encoder Representations from Transformers (BERT) to recognize rare diseases and their clinical manifestations (signs and symptoms). Results: BioBERT, a domain-specific language representation based on BERT and trained on biomedical corpora, obtains the best results with an F1 of 85.2% for rare diseases. Since many signs are usually described by complex noun phrases that involve the use of use of overlapped, nested and discontinuous entities, the model provides lower results with an F1 of 57.2%. Conclusions: While our results are promising, there is still much room for improvement, especially with respect to the identification of clinical manifestations (signs and symptoms).en
dc.description.sponsorshipFunding: This work is part of the R &D &i ACCESS2MEET project (PID2020-116527RB-I0), financed by MCIN AEI/10.13039/501100011033/. This work was also supported by the Community of Madrid under the Interdisciplinary Projects Program for Young Researchers (NLP4Rare-CM-UC3M project) and the line of Excellence of University Professors (EPUC3M17).en
dc.format.extent23es
dc.identifier.bibliographicCitationSegura-Bedmar, I., Camino-Perdonas, D., & Guerrero-Aspizúa, S. (2022). Exploring deep learning methods for recognizing rare diseases and their clinical manifestations from texts. BMC Bioinformatics, 23(1).en
dc.identifier.doihttps://doi.org/10.1186/s12859-022-04810-y
dc.identifier.issn1471-2105
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue263es
dc.identifier.publicationlastpage23es
dc.identifier.publicationtitleBMC BIOINFORMATICSen
dc.identifier.publicationvolume23es
dc.identifier.urihttps://hdl.handle.net/10016/39125
dc.identifier.uxxiAR/0000032111
dc.language.isoenges
dc.publisherSpringer Natureen
dc.relation.datasethttps://doi.org/10.21950/S2IRKE
dc.relation.datasethttps://doi.org/10.21950/DEURZF
dc.relation.projectIDGobierno de España. PID2020-116527RB-I00es
dc.relation.projectIDComunidad de Madrid. NLP4Rare-CM-UC3Mes
dc.rights© The Author(s) 2022en
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.otherRare diseasesen
dc.subject.otherNamed entity recognitionen
dc.subject.otherDeep learningen
dc.titleExploring deep learning methods for recognizing rare diseases and their clinical manifestations from textsen
dc.typeresearch articleen
dspace.entity.typePublication
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