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Designing an effective semantic fluency test for early MCI diagnosis with machine learning

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dc.contributor.authorGómez-Valades Batanero, Alba
dc.contributor.authorMartínez Tomás, Rafael
dc.contributor.authorRincón Zamorano, Mariano
dc.date.accessioned2024-12-09T11:17:17Z
dc.date.available2024-12-09T11:17:17Z
dc.date.issued2024-08-16
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en Comput. Biol. Med. 180, 108955 (2024). 14., está disponible en línea en el sitio web del editor: https://doi.org/10.1016/j.compbiomed.2024.108955 The recorded version of this article, first published on Comput. Biol. Med. 180, 108955 (2024). 14., is available online on the publisher's website: https://doi.org/10.1016/j.compbiomed.2024.108955
dc.description.abstractSemantic fluency tests are one of the key tests used in batteries for the early detection of Mild Cognitive Impairment (MCI) as the impairment in speech and semantic memory are among the first symptoms, attracting the attention of a large number of studies. Several new semantic categories and variables capable of providing complementary information of clinical interest have been proposed to increase their effectiveness. However, this also extends the time required to complete all tests and get the overall diagnosis. Therefore, there is a need to reduce the number of tests in the batteries and thus the time spent on them while maintaining or increasing their effectiveness. This study used machine learning methods to determine the smallest and most efficient combination of semantic categories and variables to achieve this goal. We utilized a database containing 423 assessments from 141 subjects, with each subject having undergone three assessments spaced approximately one year apart. Subjects were categorized into three diagnostic groups: Healthy (if diagnosed as healthy in all three assessments), stable MCI (consistently diagnosed as MCI), and heterogeneous MCI (when exhibiting alternations between healthy and MCI diagnoses across assessments). We obtained that the most efficient combination to distinguish between these categories of semantic fluency tests included the animals and clothes semantic categories with the variables corrects, switching, clustering, and total clusters. This combination is ideal for scenarios that require a balance between time efficiency and diagnosis capability, such as population-based screenings.en
dc.description.versionversión publicada
dc.identifier.citationAlba Gómez-Valadés, Rafael Martínez, Mariano Rincón. Designing an effective semantic fluency test for early MCI diagnosis with machine learning. Comput. Biol. Med. 180. 108955 2024. 14. https://doi.org/10.1016/j.compbiomed.2024.108955
dc.identifier.doihttps://doi.org/10.1016/j.compbiomed.2024.108955
dc.identifier.issn0010-4825; eISSN: 1879-0534
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24754
dc.journal.titleComputers in Biology and Medicine
dc.journal.volume180
dc.language.isoen
dc.publisherElsevier
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsSemantic fluency testen
dc.subject.keywordsMCIen
dc.subject.keywordsmachine learningen
dc.subject.keywordsswitchingen
dc.subject.keywordsclusteringen
dc.subject.keywordsearly diagnosisen
dc.titleDesigning an effective semantic fluency test for early MCI diagnosis with machine learningen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication05104596-1880-4adc-8bfd-cf92aa718ee0
relation.isAuthorOfPublicationc87ba267-e907-4b5f-ad5f-319c1cb3d3cd
relation.isAuthorOfPublication06a389cc-435e-4713-a8d7-8350f08ea1a8
relation.isAuthorOfPublication.latestForDiscovery05104596-1880-4adc-8bfd-cf92aa718ee0
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