Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/77148
Authors: 
Morik, Katharina
Brockhausen, Peter
Joachims, Thorsten
Year of Publication: 
1999
Series/Report no.: 
Technical Report, SFB 475: Komplexitätsreduktion in Multivariaten Datenstrukturen, Universität Dortmund 1999,24
Abstract: 
The 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.
Document Type: 
Working Paper

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