Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring.
We present an analysis of the computational features of neural networks and fuzzy logic architectures which attempts to explain their recent popularity as well as their drawbacks. Based upon many reports in several fields, we identify the key computational requirements in the clinical laboratory setting, and review several classical tools. In particular we make the observation that all of these needs may be viewed as a search for an appropriate mathematical mapping. We suggest that the neural networks promise as a universal function approximant is the main source of its apparent attractivity. We then describe a customized neural network architecture as a non-linear, adaptive signal processor for integrated monitoring. This architecture is employed in the Adaptive Real-Time Anesthesiologist Associate (ARTAA) system, which has been developed as a joint project at the Department of Anesthesiology, Albert Einstein Medical Center and the Electrical and Computer Engineering Department, Drexel University in Philadelphia, USA. In this application the neural network realizes a non-linear scalar map from the set of physiological signals to a vital function status (VFS) indicator. The system is now under clinical testing.
Guez
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Electrical and Computer Engineering Department, Drexel University, Philadelphia, PA 19104, USA. guezal@dunx1.ocs.drexel.edu
Nevo
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D000758:Anesthesia / Q000009:adverse effects / Q000379:methods; D002984:Clinical Laboratory Information Systems; D017143:Fuzzy Logic; D006801:Humans; D008432:Mathematical Computing; D008991:Monitoring, Physiologic / Q000379:methods*; D016571:Neural Networks, Computer