Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions.
In this contribution we present an application of a knowledge-based neural network technique in the domain of medical research. We consider the crucial problem of intensive care patients developing a septic shock during their stay at the intensive care unit. Septic shock is of prime importance in intensive care medicine due to its high mortality rate. Our analysis of the patient data is embedded in a medical data analysis cycle, including preprocessing, classification, rule generation and interpretation. For classification and rule generation we chose an improved architecture based on a growing trapezoidal basis function network for our metric variables. Our results extend those of a black box classification and give a deeper insight in our patient data. We evaluate our results with classification and rule performance measures. For feature selection we introduce a new importance measure.
Paetz
Jürgen
J
J.W. Goethe-Universität Frankfurt am Main, Fachbereich Biologie und Informatik, Institut für Informatik, Robert-Mayer-Strasse 11-15, D-60054 Frankfurt am Main, Germany. paetz@cs.uni-frankfurt.de
D000005:Abdomen; D000465:Algorithms; D017143:Fuzzy Logic; D005858:Germany / Q000453:epidemiology; D006801:Humans; D007362:Intensive Care Units; D016571:Neural Networks, Computer; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D012772:Shock, Septic / Q000175:diagnosis / Q000401:mortality*; D016019:Survival Analysis