Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/111915
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dc.contributor.authorSuárez Araujo, Carmen Pazen_US
dc.contributor.authorGarcía Báez, Patricioen_US
dc.contributor.authorCabrera León, Ylermien_US
dc.contributor.authorProchazka, Alesen_US
dc.contributor.authorRodríguez Espinosa, Norbertoen_US
dc.contributor.authorFernández Viadero, Carlosen_US
dc.contributor.editorBangyal, Waqas Haider-
dc.date.accessioned2021-09-24T09:07:06Z-
dc.date.available2021-09-24T09:07:06Z-
dc.date.issued2021en_US
dc.identifier.issn1748-670Xen_US
dc.identifier.urihttp://hdl.handle.net/10553/111915-
dc.description.abstractClinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.en_US
dc.languageengen_US
dc.relationAlzheimer’s Disease Neuroimaging Initiative (ADNI) U01 AG024904en_US
dc.relationDOD ADNI (Department of Defense award number W81XWH-12-2-0012)en_US
dc.relation.ispartofComputational and Mathematical Methods in Medicineen_US
dc.sourceComputational and Mathematical Methods in Medicine [ISSN 1748-670X], v. 2021, 5545297en_US
dc.subject3314 Tecnología médicaen_US
dc.titleA real-time clinical decision support system, for mild cognitive impairment detection, based on a hybrid neural architectureen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1155/2021/5545297en_US
dc.identifier.pmid34257699-
dc.identifier.scopus2-s2.0-85110235245-
dc.contributor.orcid0000-0002-8826-0899-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.contributor.wosstandardBangyal, Waqas Haider-
dc.contributor.wosstandardBangyal, Waqas Haider-
dc.contributor.wosstandardBangyal, Waqas Haider-
dc.contributor.wosstandardBangyal, Waqas Haider-
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr0,522
dc.description.jcr2,809
dc.description.sjrqQ2
dc.description.jcrqQ2
dc.description.scieSCIE
dc.description.miaricds10,8
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.orcid0000-0001-5709-2274-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
crisitem.author.fullNameGarcía Baez,Patricio-
crisitem.author.fullNameCabrera León, Ylermi-
crisitem.author.fullNameFernández Viadero,Carlos-
Colección:Artículos
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