Artificial Neural Networks: A Tutorial
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive mixtures of local experts
Neural Computation
Mortality assessment in intensive care units via adverse events using artificial neural networks
Artificial Intelligence in Medicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Using intelligence techniques to predict postoperative morbidity of endovascular aneurysm repair
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
An analytic approach to better understanding and management of coronary surgeries
Decision Support Systems
Risk prediction for postoperative morbidity of endovascular aneurysm repair using ensemble model
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part III
Journal of Medical Systems
Predicting the impact of hospital health information technology adoption on patient satisfaction
Artificial Intelligence in Medicine
CardioSmart365: artificial intelligence in the service of cardiologic patients
Advances in Artificial Intelligence - Special issue on Artificial Intelligence Applications in Biomedicine
Applying a BP neural network model to predict the length of hospital stay
HIS'13 Proceedings of the second international conference on Health Information Science
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Background: The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritise medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients. Methods: A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset. Results: The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90. Conclusions: This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.