About adaptive state knowledge extraction for septic shock mortality prediction
- The early prediction of mortality is one of the unresolved tasks in intensive care medicine. This contribution models medical symptoms as observations cased by transitions between hidden markov states. Learning the underlying state transition probabilities results in a prediction probability success of about 91%. The results are discussed and put in relation to the model used. Finally, the rationales for using the model are reflected: Are there states in the septic shock data?
| Author: | Rüdiger W. BrauseGND |
|---|---|
| URN: | urn:nbn:de:hebis:30-79206 |
| Document Type: | Conference Proceeding |
| 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 |
| Note: | Postprint, zuerst in: Proc. of the 14th IEEE International Conference of Tools with Artificial Intelligence ICTAI 02, Washington DC, IEEE press, Los Alamitos, CA 2002, S.. 3-8 |
| Source: | IEEE, 14th International Conference on Tools with Artificial Intelligence, ICTAI-2002, IEEE Press, 2002, pp. 3-8 |
| HeBIS-PPN: | 227742176 |
| Institutes: | Informatik und Mathematik / Informatik |
| Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik |
| Sammlungen: | Universitätspublikationen |
| Licence (German): |  Deutsches Urheberrecht |
