Expert systems in histopathology. II. Knowledge representation and rule-based systems.
Two aspects of expert systems for use in diagnostic histopathology and cytopathology are examined: knowledge representation and the structure and operation of rule-based systems. Knowledge may be represented, e.g., in semantic networks, frames, multiple contexts and model-based structures; the choice of structure should be matched to the type of information to create an efficient and logically adequate expert system. In a rule-based system, knowledge is represented as "rules," often in the form of "IF (condition)-THEN (conclusion)" rules. The anatomy of such rules and their operation is explored via the use of examples. Uncertainty in rules is briefly addressed, and their processing by the symbolic reasoning of the "inference engine" of the expert system is described, including both "forward-chaining" ("data-driven") operations and "backward-chaining" ("goal-driven") operations.
Bartels
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Optical Sciences Center, University of Arizona, Tucson 85721.
Hiessl
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D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006653:Histology; D010336:Pathology