Some new results on neural network approximation
Neural Networks
Why some feedforward networks cannot learn some polynomials
Neural Computation
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Data preparation for data mining
Data preparation for data mining
Principles of data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Decision Support Systems and Intelligent Systems (7th Edition)
Decision Support Systems and Intelligent Systems (7th Edition)
Simultaneous evolution of neural network topologies and weights for classification and regression
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Weight-elimination neural networks applied to coronary surgery mortality prediction
IEEE Transactions on Information Technology in Biomedicine
Uniqueness of medical data mining
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Data strip mining for the virtual design of pharmaceuticals with neural networks
IEEE Transactions on Neural Networks
Artificial Intelligence in Medicine
Discovery and inclusion of SOFA score episodes in mortality prediction
Journal of Biomedical Informatics
Mining lung cancer patient data to assess healthcare resource utilization
Expert Systems with Applications: An International Journal
Discovery and Integration of Organ-Failure Episodes in Mortality Prediction
AIME '07 Proceedings of the 11th conference on Artificial Intelligence in Medicine
A grid data mining architecture for learning classifier systems
WSEAS Transactions on Computers
Supervised learning classifier systems for grid data mining
CIS'09 Proceedings of the international conference on Computational and information science 2009
Adaptive decision support for intensive care
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Journal of Biomedical Informatics
Applied Computational Intelligence and Soft Computing
Using sensitivity analysis and visualization techniques to open black box data mining models
Information Sciences: an International Journal
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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Objective: This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. Materials and methods: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires 17 static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13,164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. Results: The event based models predicted the outcome more accurately than the currently used SAPS II model (P