Application of generalized dynamic neural networks to biomedical data.
This paper reviews the application of continuous recurrent neural networks with time-varying weights to pattern recognition tasks in medicine. A general learning algorithm based on Pontryagin's maximum principle is recapitulated, and possibilities of improving the generalization capabilities of these networks are given. The effectiveness of the methods is demonstrated by three different real-world examples taken from the fields of anesthesiology, orthopedics, and radiology.
Leistritz
Lutz
L
Institute of Medical Statistics, Computer Sciences, and Documentation, Friedrich Schiller University Jena, Jena 07740, Germany. i6lelu@imsid.uni-jena.de
Galicki
Miroslaw
M
Kochs
Eberhard
E
Zwick
Ernst Bernhard
EB
Fitzek
Clemens
C
Reichenbach
Jürgen R
JR
Witte
Herbert
H
D000465:Algorithms; D001698:Biomedical Engineering / Q000379:methods*; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted