Knowledge-Acquisition Tools for Medical Knowledge-Based Systems

Abstract:

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Keywords:

Knowledge-Based Systems - Knowledge Acquisition

• References

• 1 Bellazzi R, Quaglini S. Reusable influence diagrams. Artif Intell Med 1994; 6: 483-500.
• 2 Bellazzi R, Quaglini S, Berzuini C, Stefanelli M. GAMEES: a probabilistic environment for expert systems. Comp Meth Prog Biomed 1991; 35: 177-91.
• 3 Birmingham W, Klinker G. Knowledge-acquisition tools with explicit problem-solving methods. Knowl Engin Rev 1993; 8: 5-25.
• 4 Eriksson H, Musen M. Metatools for knowledge acquisition. IEEE Software 1993; May: 23-6.
• 5 Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Learning 1984; 6: 721-41.
• 6 Genesereth MR, Fikes RE. Knowledge Interchange Format, version 3.0 Reference Manual. Technical Report TRL 92-1. Stanford University, Computer Science Department; 1992
• 7 Gruber T. Towards principles for the design of ontologies used for knowledge sharing. Technical Report KSL 93-04. Stanford University; 1993
• 8 Gruber T. A translation approach to portable ontology specifications. Knowledge Acquisition 1993; 5: 199-220.
• 9 Hayes-Roth B. A blackboard architecture for control. Artif Intell 1985; 26: 251-321.
• 10 Hayes-Roth B, Pfleger K, Lalanda P, Morignot P, Balabonivic M. A domain-specific software architecture for adaptive intelligent systems. Technical Report KSL 94-11. Knowledge Systems laboratory, Stanford University; March 1994
• 11 Ironi L, Cattaneo L, Stefanelli M. A tool for pathophysiological knowledge acquisition. In: Andeassen S, Engelbrecht R, Wyatt J. eds. Fourth Conference on Artificial Intelligence in Medicine Europe. Amsterdam: IOS Press; 1993: 13-31.
• 12 Ironi L, Stefanelli M. A framework for building and simulating qualitative models of compartmental systems. Comp Meth Progr Biomed 1994; 42: 233-59.
• 13 Klinker G, Bhola C, Dallemagne G, Marques D, McDermott J. Usable and reusable programming constructs. Knowledge Acquisition 1991; 3: 117-36.
• 14 Kuipers BJ. Qualitative simulation. Artif Intell 1986; 29: 280-338.
• 15 Lanzola G, Stefanelli M. Computational model 3.0. Deliverable 25, GAMES II Consortium. 1993
• 16 Lanzola G, Stefanelli M. Inferential knowledge acquisition. Artif Intell Med 1993; 5: 253-68.
• 17 Larizza C, Moglia A, Stefanelli M. M-HTP: a system for monitoring heart transplant patients. Artif Intellig Med 1992; 4: 111-26.
• 18 Lauritzen SL, Spiegelhalter DJ. Local computations with probabilities on graphical structures and their applications to expert systems (with discussion). J Roy Statis Soc B 1988; 50: 157-224.
• 19 McDermott J. Preliminary steps toward a taxonomy of problem-solving methods. In: Automating Knowledge Acquisition for Expert Systems. Kluwer Academic Publishers; 1988: 225-56.
• 20 Musen M. Overcoming the limitations of role-limiting methods. Knowledge Acquisition 1992; 4: 165-70.
• 21 Neches R, Fikes R, Finin T. et al. Enabling technology for knowledge sharing. AI Magazine 1991; 12: 16-36.
• 22 Quaglini S, Bellazzi R, Locatelli F, Stefanelli M, Salvaneschi C. An influence diagram for assessing GVHD prophylaxis after bone marrow transplantation in children. Med Decis Making 1994; 14: 223-35.
• 23 Quaglini S, Bellazzi R, Stefanelli M, Locatelli F. Sharing and reusing therapeutic knowledge for managing leukemic children. In: Andeassen S, Engelbrecht R, Wyatt J. eds. Fourth Conference on Artificial Intelligence in Medicine Europe. Amsterdam: IOS Press; October 1993: 319-30.
• 24 Ramoni M, Stefanelli M, Magnani L, Barosi G. An epistemological framework for medical knowledge based systems. IEEE Trans Syst Man Cybem 1992; 22: 1361-74.
• 25 Shahar Y, Musen M. Knowledge acquisition for temporal-abstraction mechanisms. Knowledge Acquisition 1992; 4: 217-36.
• 26 Shahar Y, Musen M. RESUME: a temporalabstraction system for patient monitoring. Comput Biomed Res 1993; 26: 255-73.
• 27 Steels L. Components of expertise. AI Magazine 1990; 12: 38-49.
• 28 Tu SW, Shahar Y, Dawes J. et al. A problemsolving model for episodic skeletal-plan refinement. Knowledge Acquisition 1992; 4: 197-216.
• 29 van Heijst G, Falasconi S, Abu-Hanna A, Schreiber G, Stefanelli M. Principles for modularizing medical domain knowledge. (to be publ).
• 30 van Heijst G, Lanzola G, Schreiber G, Stefenelli M. Methodological foundations of medical KBS development. (to be publ).
• 31 Wielinga BJ, Scheiber ATh, Breuker JA. KADS: a modelling approach to knowledge engineering. Knowledge Acquisition 1992; 4: 5-53.