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 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.
| Author: | Rüdiger W. BrauseGND, 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: | Universitätsbibliothek Johann Christian Senckenberg |
| 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 und Mathematik / Informatik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Archivex. zur Lesesaalplatznutzung § 52b UrhG |
