14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'02) About Adaptive State Knowledge Extraction for Septic Shock Mortality Prediction Washington, DC November 04-November 06 ISBN: 0-7695-1849-4
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?
Citation:
Rüdiger W. Brause, "About Adaptive State Knowledge Extraction for Septic Shock Mortality Prediction," ictai, pp.3, 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'02), 2002 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||