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Adaptive knowledge discovery for decision support in intensive care units

2009-07Journal article Open access
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dc.contributor.authorGago, Pedro
dc.contributor.authorSantos, Manuel Filipe
dc.date.accessioned2025-05-23T10:10:03Z
dc.date.available2025-05-23T10:10:03Z
dc.date.issued2009-07
dc.descriptionhttps://scholar.google.com/scholar?q=Adaptive%20knowledge%20discovery%20for%20decision%20support%20in%20intensive%20care%20units
dc.description.abstractClinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus enhancing adaptive capacities.eng
dc.description.sponsorshipThe INTCare project is financially supported by FCT (PTDC/EIA/72819/2006)
dc.identifier.citationGago, P. E. D. R. O., & Santos, M. F. (2009). Adaptive knowledge discovery for decision support in intensive care units. WSEAS Transactions on Computers, 8(7), 1103-1112.
dc.identifier.issn11092750
dc.identifier.urihttp://hdl.handle.net/10400.8/12965
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWorld Scientific and Engineering Academy and Society (WSEAS)
dc.relationINTCARE - Intelligent Decision Support System for Intensive Care
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectClinical Decision Support
dc.subjectIntelligent Decision Support Systems
dc.subjectKnowledge Discovery
dc.subjectIntensive care
dc.subjectEnsembles
dc.subjectMulti-Agent Systems
dc.titleAdaptive knowledge discovery for decision support in intensive care unitseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleINTCARE - Intelligent Decision Support System for Intensive Care
oaire.awardURIhttp://hdl.handle.net/10400.8/12887
oaire.citation.endPage1112
oaire.citation.issue7
oaire.citation.startPage1103
oaire.citation.titleWSEAS Transactions on Computers
oaire.citation.volume8
oaire.fundingStream5876-PPCDTI
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameGago
person.givenNamePedro
person.identifier.orcid0000-0003-3404-9657
relation.isAuthorOfPublicationda8a69f8-5112-4415-b79b-bc9e2ed28131
relation.isAuthorOfPublication.latestForDiscoveryda8a69f8-5112-4415-b79b-bc9e2ed28131
relation.isProjectOfPublication13e85ba1-89fc-4101-9e99-d777a0b7225b
relation.isProjectOfPublication.latestForDiscovery13e85ba1-89fc-4101-9e99-d777a0b7225b

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Clinical Decision Support Systems (CDSS) are becoming commonplace. They are used to alert doctors about drug interactions, to suggest possible diagnostics and in several other clinical situations. One of the approaches to building CDSS is by using techniques from the Knowledge Discovery from Databases (KDD) area. However using KDD for the construction of the knowledge base used in such systems, while reducing the maintenance work still demands repeated human intervention. In this work we present a KDD based architecture for CDSS for intensive care medicine. By resorting to automated data acquisition our architecture allows for the evaluation of the predictions made and subsequent action aiming at improving the predictive performance thus enhancing adaptive capacities.
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