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dc.contributor.authorBrockhausen, Peterde
dc.contributor.authorJoachims, Thorstende
dc.contributor.authorMorik, Katharinade
dc.date.accessioned2004-12-06T18:40:33Z-
dc.date.available2004-12-06T18:40:33Z-
dc.date.issued1999de
dc.identifier.urihttp://hdl.handle.net/2003/4959-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-3088-
dc.description.abstractThe paper describes a case study in combining different methods for acquiring medical knowledge. Given a huge amount of noisy, high dimensional numerical time series data describing patients in intensive care, the support vector machine is used to learn when and how to change the dose of which drug. Given medical knowledge about and expertise in clinical decision making, a first-order logic knowledge base about effects of therapeutical interventions has been built. As a preprocessing mechanism it uses another statistical method. The integration of numerical and knowledge-based procedures eases the task of validation in two ways. On one hand, the knowledge base is validated with respect to past patients' records. On the other hand, medical interventions that are recommended by learning results are justified by the knowledge base.en
dc.format.extent199357 bytes-
dc.format.extent228308 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypeapplication/postscript-
dc.language.isoende
dc.publisherUniversitätsbibliothek Dortmundde
dc.subject.ddc310de
dc.titleCombining statistical learning with a knowledge-based approachen
dc.title.alternativeA case study in intensive care monitoringen
dc.typeTextde
dc.type.publicationtypereporten
dcterms.accessRightsopen access-
Appears in Collections:Sonderforschungsbereich (SFB) 475

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