Septic shock diagnosis by neural networks and rule based systems

In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural ne
In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies. Our approach includes necessary steps of data mining, i.e. building up a data base, cleaning and preprocessing the data and finally choosing an adequate analysis for the medical patient data. We chose two architectures based on supervised neural networks. The patient data is classified into two classes (survived and deceased) by a diagnosis based either on the black-box approach of a growing RBF network and otherwise on a second network which can be used to explain its diagnosis by human-understandable diagnostic rules. The advantages and drawbacks of these classification methods for an early warning system are discussed.
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Metadaten
Author:Rüdiger W. Brause, Fred Henrik Hamker, Jürgen Paetz
URN:urn:nbn:de:hebis:30-79197
Document Type:Part of a Book
Language:English
Date of Publication (online):2010/09/08
Year of first Publication:2002
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/09/08
Source:in: Schmitt, M.; Teodorescu, H.-N.; Jain, A.; Jain, A.; Jain, S.; Jain, L.C., (eds.): Computational intelligence techniques in medical diagnosis and prognosis, Springer-Verl., New York, 2002, pp. 323-356
HeBIS PPN:227739434
Institutes:Informatik
Dewey Decimal Classification:004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License LogoArchivex. zur Lesesaalplatznutzung § 52b UrhG

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