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A keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reports

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dc.contributor.authorFabregat Marcos, Hermenegildo
dc.contributor.authorDuque Fernández, Andrés
dc.contributor.authorAraujo Serna, M. Lourdes
dc.contributor.authorMartínez Romo, Juan
dc.date.accessioned2024-06-11T15:15:27Z
dc.date.available2024-06-11T15:15:27Z
dc.date.issued2021
dc.description.abstractBackground and objectives: The 10th version of International Classification of Diseases (ICD-10) codification system has been widely adopted by the health systems of many countries, including Spain. However, manual code assignment of Electronic Health Records (EHR) is a complex and time-consuming task that requires a great amount of specialised human resources. Therefore, several machine learning approaches are being proposed to assist in the assignment task. In this work we present an alternative system for automatically recommending ICD-10 codes to be assigned to EHRs. Methods: Our proposal is based on characterising ICD-10 codes by a set of keyphrases that represent them. These keyphrases do not only include those that have literally appeared in some EHR with the considered ICD-10 codes assigned, but also others that have been obtained by a statistical process able to capture expressions that have led the annotators to assign the code. Results: The result is an information model that allows to efficiently recommend codes to a new EHR based on their textual content. We explore an approach that proves to be competitive with other state-of-the-art approaches and can be combined with them to optimise results. Conclusions: In addition to its effectiveness, the recommendations of this method are easily interpretable since the phrases in an EHR leading to recommend an ICD-10 code are known. Moreover, the keyphrases associated with each ICD-10 code can be a valuable additional source of information for other approaches, such as machine learning techniques.en
dc.description.versionversión publicada
dc.identifier.doihttps://doi.org/10.1016/j.artmed.2021.102177
dc.identifier.issn0933-3657
dc.identifier.urihttps://hdl.handle.net/20.500.14468/22409
dc.journal.titleArtificial Intelligence in Medicine
dc.journal.volume121
dc.language.isoen
dc.publisherElsevier
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject.keywordsMedical records
dc.subject.keywordsICD-10 codes
dc.subject.keywordsKeyphrase extraction
dc.subject.keywordsInterpretability
dc.titleA keyphrase-based approach for interpretable ICD-10 code classification of Spanish medical reportses
dc.typejournal articleen
dc.typeartículoes
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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