A BAYESIAN RECURRENT NEURAL NETWORK FOR UNSUPERVISED PATTERN RECOGNITION IN LARGE INCOMPLETE DATA SETS

A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigate...

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Bibliographic Details
Published in:International journal of neural systems 2005-06, Vol.15 (3), p.207-222
Main Authors: ORRE, ROLAND, BATE, ANDREW, NORÉN, G. NIKLAS, SWAHN, ERIK, ARNBORG, STEFAN, EDWARDS, I. RALPH
Format: Article
Language:English
Subjects:
adverse drug reactions
Algorithms
Antipsychotic Agents - adverse effects
Artificial Intelligence
Bayes Theorem
Cluster Analysis
clustering
Creatine Kinase - blood
Data Interpretation, Statistical
Databases, Factual
dysphagia
Hopfield network
Humans
Mental Recall
Neural Networks (Computer)
neuroleptic malignant syndrome
Neuroleptic Malignant Syndrome - epidemiology
Pattern Recognition, Automated
therapy
unsupervised pattern recognition
World Health Organization
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Summary:A recurrent neural network, modified to handle highly incomplete training data is described. Unsupervised pattern recognition is demonstrated in the WHO database of adverse drug reactions. Comparison is made to a well established method, AutoClass, and the performances of both methods is investigated on simulated data. The neural network method performs comparably to AutoClass in simulated data, and better than AutoClass in real world data. With its better scaling properties, the neural network is a promising tool for unsupervised pattern recognition in huge databases of incomplete observations.
ISSN:0129-0657
1793-6462
1793-6462
DOI:10.1142/S0129065705000219