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Closed loop knowledge discovery for decision support in intensive care medicine

2009-07Conference paper Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorGago, Pedro
dc.contributor.authorSantos, Manuel Filipe
dc.date.accessioned2025-06-09T17:08:15Z
dc.date.available2025-06-09T17:08:15Z
dc.date.issued2009-07
dc.description13th WSEAS International Conference on Computers - Held as part of the 13th WSEAS CSCC Multiconference, 23 July 2009 through 25 July 2009 - Code 79164
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 closing the KDD loop.eng
dc.description.sponsorshipThe INTCare project is financially supported by FTC (PTDC/EIA/72819/2006).
dc.identifier.citationGago, P. E. D. R. O., & Santos, M. F. (2009, July). Closed loop knowledge discovery for decision support in intensive care medicine. In WSEAS International Conference. Proceedings. Recent Advances in Computer Engineering (No. 13). WSEAS.
dc.identifier.isbn978-960-474-099-4
dc.identifier.issn1790-5109
dc.identifier.urihttp://hdl.handle.net/10400.8/13192
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWorld Scientific and Engineering Academy and Society (WSEAS)
dc.relationINTCARE - Intelligent Decision Support System for Intensive Care
dc.relation.hasversionhttps://dl.acm.org/doi/10.5555/1627695.1627778
dc.rights.uriN/A
dc.subjectClinical Decision Support
dc.subjectIntelligent Decision Support Systems
dc.subjectKnowledge Discovery
dc.subjectIntensive care
dc.subjectEnsembles
dc.subjectMulti-Agent Systems
dc.titleClosed loop knowledge discovery for decision support in intensive care medicineeng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardTitleINTCARE - Intelligent Decision Support System for Intensive Care
oaire.awardURIhttp://hdl.handle.net/10400.8/12887
oaire.citation.conferenceDate2009-07
oaire.citation.conferencePlaceRodos, Greece
oaire.citation.endPage452
oaire.citation.startPage447
oaire.citation.titleProceedings of the 13th WSEAS International Conference on Computers
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 closing the KDD loop.
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