System theoretic approach to medical diagnosis

A mathematical model for an adaptive expert system in anesthesia is presented. The concept of clusters that utilize clinical attributes in order to reduce the dimensionality of the patient's state-space is introduced. One goal of the model is to implement the existing categories and to identify and cluster categories as well as make the system adaptive to new and more optimal categories. Well-known techniques of pattern classification and cluster analysis are used on the measurable dataset to look for new categories or to readjust existing ones. Readjustment is required to optimize the existing categories to give the most efficient classification of the diseases.<
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