An intelligent diagnostic support system (I-DSS) for decision-making support in diagnostic processes is presented. I-DSS can be placed between diagnosis carried out by a human diagnostician, without any automatic support, and diagnosis carried out in a fully automatic way. Fully automatic diagnosis may be appealing if used in very complex domains and if the user is non-expert. However, in the case of an expert user, a fully automatic approach is not suitable. In the fully automatic approach the system should be equipped with a strategic knowledge base (the knowledge needed for making the ’best’ choice) and as a consequence the expert user is prevented from making decisions on the basis of his or her own experience. This restriction causes, in general, a sort of psychological rejection, on the part of the expert user, of the traditional fully automatic approach. This is particularly true in those domains, such as medicine, where there is more than one approach to the solution and it is seldom that one approach can be considered ’right’ and the others ’wrong’. Experience related to diagnostic expert systems applications shows that, whenever trade-off problems arise in choosing between alternative actions, it is preferable to leave decisions to the expert. Starting from these considerations we present a system (I-DSS) which, without being ’intrusive’, aims to be an effective support for the decision-maker during the diagnostic process.
[1]
Brian C. Williams,et al.
Diagnosing Multiple Faults
,
1987,
Artif. Intell..
[2]
Silvano Mussi,et al.
Acquiring and using strategic knowledge in diagnostic processes
,
1991
.
[3]
Thomas R. Gruber.
Acquiring Strategic Knowledge from Experts
,
1988,
Int. J. Man Mach. Stud..
[4]
S. M. Watkins,et al.
Common Medical Diagnoses: An Algorithmic Approach
,
1990
.
[5]
Joe W. Duran,et al.
A General Expert System Design for Diagnostic Problem Solving
,
1984,
IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6]
Edward H. Shortliffe,et al.
Computer-based medical consultations, MYCIN
,
1976
.
[7]
Paul R. Cohen,et al.
MU: A Development Environment for Prospective Reasoning Systems
,
1987,
AAAI.
[8]
P. Miller,et al.
A critiquing approach to expert computer advice--ATTENDING
,
1984
.
[9]
Edward H. Shortliffe,et al.
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
,
1984
.