Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Machine Learning
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
Artificial Neural Networks in Biomedicine
Artificial Neural Networks in Biomedicine
Self-Organizing Maps
Metric Rule Generation with Septic Shock Patient Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Enhancing Rule Interestingness for Neuro-fuzzy Systems
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Clinical Knowledge Discovery in Hospital Information Systems: Two Case Studies
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
About the Analysis of Septic Shock Patient Data
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Discriminative Power of Input Features in a Fuzzy Model
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Integration of Neural Networks and Knowledge-Based Systems in Medicine
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Extracting rules from trained neural networks
IEEE Transactions on Neural Networks
Functional equivalence between radial basis function networks and fuzzy inference systems
IEEE Transactions on Neural Networks
Optimization study with ligand-design interval rules
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Computers in Biology and Medicine
Diagnosis of psychosomatic disorders using radial basis functions network
EHAC'05 Proceedings of the 4th WSEAS International Conference on Electronics, Hardware, Wireless and Optical Communications
Integration of an artificial neural network to predict electrical energy consumption
ISTASC'08 Proceedings of the 8th conference on Systems theory and scientific computation
Applied Soft Computing
An investigation of neuro-fuzzy systems in psychosomatic disorders
Expert Systems with Applications: An International Journal
A neuro-fuzzy approach to virtual screening in molecular bioinformatics
Fuzzy Sets and Systems
Generalization rules for binarized descriptors
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Supervised neuro-fuzzy clustering for life science applications
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
An intelligent model for the classification of children's occupational therapy problems
Expert Systems with Applications: An International Journal
Finding optimal decision scores by evolutionary strategies
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Missing data in medical databases: Impute, delete or classify?
Artificial Intelligence in Medicine
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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.