Artificial Neural Networks: Approximation and Learning Theory
Artificial Neural Networks: Approximation and Learning Theory
Metric Rule Generation with Septic Shock Patient Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Neuro-fuzzy Based Alarm System for Septic Shock Patients with a Comparison to Medical Scores
ISMDA '02 Proceedings of the Third International Symposium on Medical Data Analysis
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The clinical treatment of sepsis is one of most severe issues in hospitals. Unfortunately, until now it has not been possible to significantly reduce the mortality rate of severe forms of sepsis like septic shock, which is as high as 50-60% worldwide. Often, the diagnosis and awareness for possible implications of sepsis can be facilitated by an automated online diagnosis. This contribution reports the development of a monitoring alarm system for the individual prediction of death based on the data of 382 patients with septic shock. The paper discusses the pros and cons of such a prediction system used in a medical environment, its principal usage issues and implementation.