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

Página do item simplificado
dc.contributor.authorSouza A.A.
dc.contributor.authorAlmeida D.C.
dc.contributor.authorBarcelos T.S.
dc.contributor.authorBortoletto R.C.
dc.contributor.authorMunoz R.
dc.contributor.authorWaldman H.
dc.contributor.authorGoes M.A.
dc.contributor.authorSilva L.A.
dc.date.accessioned2024-03-12T19:10:10Z
dc.date.available2024-03-12T19:10:10Z
dc.date.issued2023
dc.description.abstract© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.The 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.
dc.description.firstpage3295
dc.description.issuenumber6
dc.description.lastpage3306
dc.description.volume27
dc.identifier.doi10.1007/s00500-021-05810-5
dc.identifier.issn1433-7479
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/34106
dc.relation.ispartofSoft Computing
dc.rightsAcesso Restrito
dc.subject.otherlanguageCovid-19 diagnostic
dc.subject.otherlanguageSARS-CoV-2
dc.subject.otherlanguageSelf-organizing maps
dc.titleSimple hemogram to support the decision-making of COVID-19 diagnosis using clusters analysis with self-organizing maps neural network
dc.typeArtigo
local.scopus.citations11
local.scopus.eid2-s2.0-85105987791
local.scopus.subjectBlood test
local.scopus.subjectClinical data
local.scopus.subjectCoronaviruses
local.scopus.subjectCovid-19 diagnostic
local.scopus.subjectDecisions makings
local.scopus.subjectMachine-learning
local.scopus.subjectSelf-organizing map neural network
local.scopus.subjectSelf-organizing-maps
local.scopus.subjectSimple++
local.scopus.subjectSoft computing approaches
local.scopus.updated2024-12-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85105987791&origin=inward
Arquivos
Coleções
Artigos de periódico