It is noted that no one method is best with a view to the potential role of automated decision for all applications. However, emphasis is given to the limitations of early work that have made artificial intelligence techniques and knowledge aids in that domain 1611.

A clinical investigator graphical capabilities which can plot specific parameters for a keeping the records of his study patients on such a system can patient over time 1126]. However, it is in the analysis of stored use the program's statistical capabilities for data analysis.

In this paper we examine the selection.

Almost one-half of the total cost of drugs spent in treating hospitalized patients is spend on antibiotics (1,2), and a significant part of this therapy is associated with serious misuse (2,3,4,5). One problem involves incorrect selection of a therapeutic regimen [4], while another involves the incorrect decision to administer any antibiotic (2,4,5). For example, one recent study concluded that one out of every four people in the United States was given penicillin during a recent year, and nearly 907. of these preL,criptions were oahccessary (6).

How could one represent with a single prototype the class of figures which have an even number of disconnected parts?

How could one represent with a single prototype the class of figures which have an even number of disconnected parts?

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

Case-based reasoning is used extensively by people in A second driving force in the evolutionary history of CBR both expert and commonsense situations. It provides a was dissatisfaction with rule-based reasoning (expert systems wide range of advantages.

How do practicing physicians make clinical decisions? What techniques can we use in the computer to produce programs that exhibit medical expertise? Our interest in these questions is motivated by our desire: 1. to provide (by computer) expert medical consultation to general practitioners or paramedical personnel in communities where such consultation is normally unavailable; 2. to come to understand the reasoning processes of expert doctors so that we may improve the teaching of their skills to medical students; and 3. to advance the techniques of artificial intelligence, especially as applied to medicine (AIM), to support our other goals. In other publications, we have described research by our group on programs to take the history of the present illness of a patient with renal disease (Pauker and Gorry, 1976; Szolovits and Pauker, 1976) and to advise the physician in the administration of the drug digitalis to patients with heart disease (Gorry et al., 1978; Silverman, 1975; Swartout, 1977).

The. development of automated assistance for medical diagnosis and decision making is an area of both theoretical and practical interest. Of methods for utilizing evidence to select diagnoses or decisions, probability theory has the firmest appeal. Probability theory in the form of Bayes' Theorem has been used by a number of" workers (Ross, 1972). Notable among recent developments are those of de Dombal and coworkers (de Dombal, 1973; de Dombal et al., 1974; 1975) and Pipberger and coworkers (Pipberger et al., 1975). The usefulness of Bayes' Theorem is limited practical difficulties, principally the lack of data adequate to estimate accurately the a priori and conditional probabilities used in the theorem. One attempt to mitigate this problem has been to assume statistical independence among various pieces of evidence. How seriously this approximation affects results is often unclear, and correction mechanisms have been explored (Ross, 1972; Norusis and Jacquez, 1975a; 1975b). Even the independence assumption requires an unmanageable number of estimates of" probabilities for most applications with realistic complexity.