HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
A recurrent neural network classifier for Doppler ultrasound blood flow signals
Pattern Recognition Letters
Combining Neural Network Models for Automated Diagnostic Systems
Journal of Medical Systems
Expert systems for time-varying biomedical signals using eigenvector methods
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Time-varying biomedical signals analysis with multiclass support vector machines
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Analysis of EEG signals by combining eigenvector methods and multiclass support vector machines
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Multiclass support vector machines for diagnosis of erythemato-squamous diseases
Expert Systems with Applications: An International Journal
Support vector machines for detection of electrocardiographic changes in partial epileptic patients
Engineering Applications of Artificial Intelligence
Decision support systems for time-varying biomedical signals: EEG signals classification
Expert Systems with Applications: An International Journal
Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks
Digital Signal Processing
Combined neural network model employing wavelet coefficients for EEG signals classification
Digital Signal Processing
Statistics over features of ECG signals
Expert Systems with Applications: An International Journal
Analysis of spike-wave discharges in rats using discrete wavelet transform
Computers in Biology and Medicine
Applied Soft Computing
Using Support Vector Machines for feature-oriented profile-based recommendations
International Journal of Advanced Intelligence Paradigms
Implementation of automated diagnostic systems: ophthalmic arterial disorders detection case
International Journal of Systems Science
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Lyapunov exponents/probabilistic neural networks for analysis of EEG signals
Expert Systems with Applications: An International Journal
Control of unstable nonlinear and nonstationary systems using LAMSTAR neural networks
ISC '07 Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control
Feature extraction from Doppler ultrasound signals for automated diagnostic systems
Computers in Biology and Medicine
A modified mixture of experts network structure for ECG beats classification with diverse features
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Journal of Medical Systems
Survey on the use of smart and adaptive engineering systems in medicine
Artificial Intelligence in Medicine
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Describes the application of a LAMSTAR (LArge Memory STorage And Retrieval) neural network to medical diagnosis and medical information retrieval problems. The network is based on M.L. Minsky's (1980) knowledge lines (k-lines) theory of memory storage and retrieval in the central nervous system. It employs arrays of self-organized map modules, such that the k-lines are implemented via link weights (address correlation) that are updated by learning. The network also employs features of forgetting and of interpolation and extrapolation, and is thus able to handle incomplete data sets. It can deal equally well with exact and fuzzy information, thus making it specifically applicable to medical diagnosis where the diagnosis is based on exact data, fuzzy patient interview information, patient histories, observed images and test records. Furthermore, the network can be operated in a closed loop with search engines to intelligently use data from the Internet in a higher learning hierarchy. All of the above features are shown to make the LAMSTAR network suitable for medical diagnosis problems that concern large data sets of many categories that are often incomplete and fuzzy. Applications of the network to three specific medical diagnosis problems are described: two from nephrology and one related to an emergency-room drug identification problem. It is shown that the LAMSTAR network is hundreds, and even thousands, times faster in its training than backpropagation-based networks when used for the same problem with exactly the same information.