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Hybrid model for early identification post-Covid-19 sequelae

2023Journal article Open access
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dc.contributor.authorde Andrade, Evandro Carvalho
dc.contributor.authorPinheiro, Luana Ibiapina C. C.
dc.contributor.authorPinheiro, Plácido Rogério
dc.contributor.authorNunes, Luciano Comin
dc.contributor.authorPinheiro, Mirian Calíope Dantas
dc.contributor.authorPereira, Maria Lúcia Duarte
dc.contributor.authorAbreu, Wilson
dc.contributor.authorFilho, Raimir Holanda
dc.contributor.authorSimão Filho, Marum
dc.contributor.authorPinheiro, Pedro Gabriel C. D.
dc.contributor.authorNunes, Rafael Espíndola Comin
dc.date.accessioned2023-10-02T09:28:54Z
dc.date.available2023-10-02T09:28:54Z
dc.date.issued2023
dc.description.abstractArtificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationde Andrade, E.C., Pinheiro, L.I.C.C., Pinheiro, P.R., Pereira, M.LD., Abreu, W., Filho, R,H., Filho, M.S., Pinheiro, P.G., Nunes, R.E.C.(2023) Hybrid model for early identification post-Covid-19 sequelae. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-023-04555-3pt_PT
dc.identifier.doi10.1007/s12652-023-04555-3pt_PT
dc.identifier.eissn1868-5145
dc.identifier.urihttp://hdl.handle.net/10400.26/46914
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringerpt_PT
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s12652-023-04555-3pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectCovid-19pt_PT
dc.subjectMachine-learningpt_PT
dc.subjectVerbal decision analysispt_PT
dc.subjectHybrid modelpt_PT
dc.subjectMedical diagnostic optimizationpt_PT
dc.subjectDecision support systemspt_PT
dc.titleHybrid model for early identification post-Covid-19 sequelaept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleJournal of Ambient Intelligence and Humanized Computingpt_PT
person.familyNameAbreu
person.givenNameWilson
person.identifier.ciencia-id0313-F7A6-AE60
person.identifier.orcid0000-0002-0847-824X
person.identifier.scopus-author-id57191608626
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication2a9bfbb4-8930-4c6c-9ed5-21d856df4a1d
relation.isAuthorOfPublication.latestForDiscovery2a9bfbb4-8930-4c6c-9ed5-21d856df4a1d

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