Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Introduction to the theory of neural computation
Introduction to the theory of neural computation
A competitive distribution theory of neocortical dynamics
Neural Computation
Learning competition and cooperation
Neural Computation
Virtual uteral inhibition in parallel activation models of associative memory
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Use of genetic algorithms for neural networks to predict community-acquired pneumonia
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
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Backpropagation neural networks have repeatedly been used for diagnostic problem-solving, but have not been demonstrated to work well when multiple disorders are present. We hypothesized that letting nodes in a backpropagation neural network compete to be part of a diagnostic solution would produce better performance than the use of existing backpropagation methods. To test this hypothesis, we derived an error backpropagation learning rule that can be used with competitive units (competitive backpropagation). Artificial neural networks were then trained using both this new learning rule and standard error backpropagation on a specific medical diagnosis problem: identification of the location of damage in the brain given a set of examination findings. Training samples included solely 'prototypical' cases where a single location of damage is present. The trained networks were then tested with atypical cases where the manifestations of more than one disorder were present or only a single manifestation was present. Networks employing competition among units were found to perform qualitatively better with these multiple-disorder cases than standard networks and also to perform better on single-manifestation cases. The reasons for this are explained. The competitive backpropagation learning rule described here provides a promising new tool for adaptive diagnostic problem-solving.