Automated assistance for maintenance of medical expert systems: the POSCH AI project

A classifier-based strategy to assist in the maintenance of expert systems is proposed. Substantiated claims about the results can be made by computing the error in probability of classification using established statistical techniques. Expert systems whose input, output, and intermediate state value(s) can be recorded as the values of a finite number of variables are considered. This class includes many expert systems including ETA and ESCA, expert systems developed by the program on surgical control of the hyperlipidemias (POSCH). Algorithmic approaches can provide automated assistance in helping tackle some issues of knowledge debugging. Some of these issues are mentioned. Details of using the algorithmic approach are covered in brief. Three types of algorithms that can be used have been identified. They are: classifier algorithms, similarity measures and generating algorithms.<>