Review of Pattern Recognition in Medical Diagnosis and Consulting Relative to a New System Model

Abstract

A review of pattern recognition applied to medical diagnosis and consulting is presented. Papers (127-references) are evaluated with respect to a new medical system model incorporating concepts of consulting and decision making, the latter based on a Bayesean approach incorporating problem knowledge. This model introduces the consultant as an essential component of the decision system. The model is that of a system with subsystems. A subsystem contains a set of classes, and each class has a class-feature relationship. Features (defined as functions of measurements), complex features, and class-feature relationships provide for introducing a priori medical knowledge and dimensionality reduction. Special features such as significant features, rule-in or rule-out features, and time-dependent features, and special classes such as subclasses and intermediate classes are introduced. Etiology, a method of tracing from one subsystem to another subsystem, also is part of the system. With respect to this model, papers in numerous application areas are reviewed. No single paper fulfills many of the system's properties, and this may be why there are very few, if any, of the results described in these papers being used in health care delivery. Part of the paper is a complementary literature review of 127 papers on computer assisted medical diagnosis and consulting.