Comparing biases for minimal network construction with back-propagation
Advances in neural information processing systems 1
Machine Learning
Artificial Neural Networks in Biomedicine
Artificial Neural Networks in Biomedicine
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Automatic covariate selection in logistic models for chest pain diagnosis: A new approach
Computer Methods and Programs in Biomedicine
Review: Application of artificial neural networks in the diagnosis of urological dysfunctions
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
Discrimination of myocardial infarction stages by subjective feature extraction
Computer Methods and Programs in Biomedicine
Artificial Intelligence in Medicine
Gastro-intestinal tract inspired computational model for myocardial infarction diagnosis
Expert Systems with Applications: An International Journal
Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks
Artificial Intelligence in Medicine
Intelligence modeling for coping strategies to reduce emergency department overcrowding in hospitals
Journal of Intelligent Manufacturing
Early prediction of the highest workload in incremental cardiopulmonary tests
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Objective: Patients with suspicion of acute coronary syndrome (ACS) are difficult to diagnose and they represent a very heterogeneous group. Some require immediate treatment while others, with only minor disorders, may be sent home. Detecting ACS patients using a machine learning approach would be advantageous in many situations. Methods and materials: Artificial neural network (ANN) ensembles and logistic regression models were trained on data from 634 patients presenting an emergency department with chest pain. Only data immediately available at patient presentation were used, including electrocardiogram (ECG) data. The models were analyzed using receiver operating characteristics (ROC) curve analysis, calibration assessments, inter- and intra-method variations. Effective odds ratios for the ANN ensembles were compared with the odds ratios obtained from the logistic model. Results: The ANN ensemble approach together with ECG data preprocessed using principal component analysis resulted in an area under the ROC curve of 80%. At the sensitivity of 95% the specificity was 41%, corresponding to a negative predictive value of 97%, given the ACS prevalence of 21%. Adding clinical data available at presentation did not improve the ANN ensemble performance. Using the area under the ROC curve and model calibration as measures of performance we found an advantage using the ANN ensemble models compared to the logistic regression models. Conclusion: Clinically, a prediction model of the present type, combined with the judgment of trained emergency department personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.