Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network

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dc.contributor.authorDe Souza, Alexandra A.
dc.contributor.authorDe Almeida, Danilo Candido
dc.contributor.authorBarcelos, Thiago S.
dc.contributor.authorCampos Bortoletto, Rodrigo
dc.contributor.authorMunoz, Roberto
dc.contributor.authorWaldman, Helio
dc.contributor.authorGoes, Miguel Angelo
dc.contributor.authorSilva, Leandro A.
dc.date.accessioned2022-11-30T02:47:00Z
dc.date.available2022-11-30T02:47:00Z
dc.date.issued2021
dc.description.abstractThe pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is related to new coronavirus disease (COVID-19) has mobilized several scientifics to explore clinical data using soft-computing approaches. In the context of machine learning, previous studies have explored supervised algorithms to predict and support diagnosis based on several clinical parameters from patients diagnosed with and without COVID-19. However, in most of them the decision is based on a “black-box” method, making it impossible to discover the variable relevance in decision making. Hence, in this study, we introduce a non-supervised clustering analysis with neural network self-organizing maps (SOM) as a strategy of decision-making. We propose to identify potential variables in routine blood tests that can support clinician decision-making during COVID-19 diagnosis at hospital admission, facilitating rapid medical intervention. Based on SOM features (visual relationships between clusters and identification of patterns and behaviors), and using linear discriminant analysis , it was possible to detect a group of units of the map with a discrimination power around 83% to SARS-CoV-2-positive patients. In addition, we identified some variables in admission blood tests (Leukocytes, Basophils, Eosinophils, and Red cell Distribution Width) that, in combination had strong influence in the clustering performance, which could assist a possible clinical decision. Thus, although with limitations, we believe that SOM can be used as a soft-computing approach to support clinician decision-making in the context of COVID-19.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameSouza_Sim2021.pdf
dc.identifier.citationSouza, A.A.d., Almeida, D.C.d., Barcelos, T.S. et al. Simple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network. Soft Comput (2021). https://doi.org/10.1007/s00500-021-05810-5en_ES
dc.identifier.doihttps://doi.org/10.1007/s00500-021-05810-5
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7550
dc.languageen
dc.publisherSpringer
dc.sourceSoft Computing
dc.subjectCOVID-19 DIAGNOSTICen_ES
dc.subjectSARS-COV-2en_ES
dc.subjectSELF-ORGANIZING MAPSen_ES
dc.titleSimple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
uv.notageneralNo disponible para descarga

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