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 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.
Orre
Roland
R
Mathematical Statistics, Stockholm University, SE-106 91 Stockholm, and NeuroLogic Sweden AB, Solna, Sweden. orre@math.su.se
Bate
Andrew
A
Norén
G Niklas
GN
Swahn
Erik
E
Arnborg
Stefan
S
Edwards
I Ralph
IR
D000465:Algorithms; D014150:Antipsychotic Agents / Q000009:adverse effects; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016000:Cluster Analysis; D003402:Creatine Kinase / Q000097:blood; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D011939:Mental Recall; D016571:Neural Networks, Computer; D009459:Neuroleptic Malignant Syndrome / Q000453:epidemiology; D010363:Pattern Recognition, Automated; D014944:World Health Organization
D014150:Antipsychotic Agents; D003402:Creatine Kinase