Scalable Software Architectures for Decision Support

M. A. Musen

doi:10.1055/s-0038-1634422

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 1999; 38(04/05): 229-238
DOI: 10.1055/s-0038-1634422

Original Article

Schattauer GmbH

Scalable Software Architectures for Decision Support

M. A. Musen

¹Stanford Medical Informatics, Stanford University School of Medicine, Stanford, CA, USA

› Institutsangaben

Abstract

Interest in decision-support programs for clinical medicine soared in the 1970s. Since that time, workers in medical informatics have been particularly attracted to rule-based systems as a means of providing clinical decision support. Although developers have built many successful applications using production rules, they also have discovered that creation and maintenance of large rule bases is quite problematic. In the 1980s, several groups of investigators began to explore alternative programming abstractions that can be used to build decision-support systems. As a result, the notions of “generic tasks” and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) problem-solving methods – domain-independent algorithms for automating stereotypical tasks – and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper highlights how developers can construct large, maintainable decision-support systems using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

Keywords

Scalable Software Architectures - Decision Support - Software Engineering - Ontologies - Problem Solving

Volltext

Referenzen

REFERENCES
1 Hripcsak G, Ludemann P, Pryor TA, Wigertz OB. Rationale for the Arden Syntax. Comp Biomed Res 1994; 27: 291-324.
2 Clancey WJ. The Epistemology of a Rule-Based Expert System: A Framework for Explanation. Artif Intell 1983; 20: 215-51.
3 Buchanan BG, Shortliffe EH. Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley; 1984
4 Buchanan BG, Shortliffe EH. Uncertainty and Evidential Support. In: Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Reading, MA: Addison-Wesley; 1984: 209-32.
5 Duda RO, Shortliffe EH. Expert Systems Research. Science 1983; 220: 261-8.
6 Bachant J. RIME: Preliminary Work Toward a Knowledge-Acquisition Tool. In: Automatic Knowledge Acquisition for Expert Systems. Marcus S. ed. Boston: Kluwer Academic Publishers; 1988: 201-24.
7 Wielinga BJ, Schrieber AT, Brueker JA. KADS: A Modelling Approach to Knowledge Engineering. Knowledge Acquisition 1992; 4 (Suppl. 01) 5-53.
8 Schreiber A, Weilinga B, Akkermans JM, van de Velde W, de Hoog R. Common-KADS: A Comprehensive Methodology for KBS Development. IEEE Expert 1994; 9: 28-37.
9 Musen MA, Gennari JH, Eriksson H, Tu SW, Puerta AR. PROTÉGÉ-II: Computer Support for Development of Intelligent Systems from Libraries of Components. In: MEDINFO ’95, Eighth World Conference on Medical Informatics. Vancouver, BC: 1995
10 Shadbolt N, Motta E, Rouge A. Constructing Knowledge-Based Systems. IEEE Software 1993; 10 (Suppl. 06) 34-8.
11 Guarino N. Understanding, Building and Using Ontologies. Int J Human-Computer Studies 1997; 42 2/3 293-310.
12 Uschold M, Gruninger M. Ontologies: Principles, Methods and Applications. The Knowledge Engineering Review 1996; 11: 93-136.
13 Fensel D, Straatman R. The Essence of Problem-Solving Methods: Making Assumptions to Gain Efficiency. Int J Human-Computer Studies 1998; 48 (Suppl. 02) 181-215.
14 McDermott J. Preliminary Steps Toward a Taxonomy of Problem-Solving Methods. In: Automatic Knowledge Acquisition for Expert Systems. Marcus S. ed. Boston: Kluwer Academic Publishers; 1988: 225-54.
15 Eriksson H, Shahar Y, Tu SW, Puerta AR, Musen MA. Task Modeling with Reusable Problem-Solving Methods. Artif Intell 1995; 79: 293-326.
16 Clancey WJ. Heuristic Classification. Artif Intell 1985; 27: 289-350.
17 Kunz JC, Shortliffe EH, Buchanan BG, Feigenbaum EA. Computer-Assisted Decision Making in Medicine. J Med Philos 1984; 9: 135-60.
18 Chandrasekaran B. Design Problem Solving: A Task Analysis. AI Magazine 1990; 11 (Suppl. 04) 59-71.
19 Eschelman L, Ehret D, McDermott J, Tan M. MOLE: A Tenacious Knowledge Acquisition Tool. Int J Man-Machine Studies 1987; 26 (Suppl. 01) 41-54.
20 Marcus S, McDermott J. SALT: A Knowledge Acquisition Language for Propose-and-Revise Systems. Artif Intell 1989; 39 (Suppl. 01) 1-37.
21 Tu SW, Kahn MG, Musen MA, Ferguson JC, Shortliffe EH, Fagan LM. Episodic skeletal-plan refinement based on temporal data. Communications of the ACM 1989; 32: 1439-55.
22 Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kauffman Publishers, Inc; 1988
23 Brueker JA, Van de Velde W. eds. The CommonKADS Library for Expertise Modeling. Amsterdam: IOS Press; 1994
24 Gil Y, Melz E. Explicit Representations of Problem-Solving Strategies to Support Knowledge Acquisition. In: National Conference on Artificial Intelligence. Portland, OR: MIT Press; 1996
25 Bylander T, Chandrasekaran B. Generic Tasks for Knowledge-Based Reasoning: The “Right” Level of Abstraction for Knowledge Acquisition. In: Knowledge Acquisition for Knowledge-Based Systems. Gains B, Boose J. eds. London: Academic Press; 1988: 65-77.
26 Schreiber A, Weilinga B, Breuker J. eds. KADS: A Principled Approach to Knowledge-Based System Development. London: Academic Press; 1993
27 Guarino N. Formal Ontology, Conceptual Analysis and Knowledge Representation. Int J of Human-Computer Studies 1995; 43: 625-40.
28 McCray AT, Nelson SJ. The Representation of Meaning in the UMLS. Method Inform Med 1995; 34 1/2 193-201.
29 Musen MA. Domain Ontologies in Software Engineering: Use of Protégé with the EON Architecture. Meth Inform Med 1998; 37: 540-50.
30 Gruber TR. A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 1992; 5: 199-220.
31 Arens Y, Knoblock CA, Shen WM. Query Reformation for Dynamic Information Integration. J Intell Inform Systems: Integrating Artif Intell Database Technol 1996; 6 2-3 99-130.
32 Rector AL, Bechhofer SB, Goble CA, Hor-rocks I, Nowlan WA, Solomon WD. The GRAIL Concept Modelling Language for Medical Terminology. Artif Intell Med 1997; 9: 139-71.
33 Musen MA, Tu SW, Das AK, Shahar Y. EON: A Component-Based Approach to Automation of Protocol-Directed Therapy. J Am Med Inform Assoc 1996; 3: 367-88.
34 Eriksson H, Puerta AR, Musen MA. Generation of Knowledge-Acquisition Tools from Domain Ontologies. Int J Human-Computer Studies 1994; 41: 425-53.
35 Shahar Y, Chen H, Stites DP, Basso L, Kaizer H, Wilson DM, Musen MA. Semiautomated Entry of Clinical Temporal-Abstraction Knowledge. J Am Med Inform Assoc. (in press).
36 Bång M, Eriksson H. Generation of Development Environments for the Arden Syntax. In: 1997 AMIA Annual Fall Symposium. Nashville, TN: Hanley & Belfus, Inc; 1997
37 Musen MA, Schreiber AT. Architectures for Intelligent Systems Based on Reusable Components. Artif Intell Med 1995; 7: 189-99.
38 Marcus S. SALT: A Knowledge-Acquisition Tool for Propose-and-Revise Systems. In: Automating Knowledge Acquisition for Expert Systems. Marcus S. ed. Boston: Kluwer Academic Publishers; 1988: 81-124.
39 Rothenfluh TE, Gennari JH, Eriksson H, Puerta AR, Tu SW, Musen MA. Reusable Ontologies, Knowledge-Acquisition Tools, and Performance Systems: PROTÉGÉ-II Solutions to Sisyphus-2. Int J Human-Computer Studies 1996; 44: 303-32.
40 Gennari JH, Altman RB, Musen MA. Reuse with PROTÉGÉ-II: From Elevators to Ribosomes. In: SSR’95: ACM-SIGSOFT Symposium on Software Reusability. Seattle, WA: 1995
41 Park JY, Musen MA. VM-in-Protégé: A Study of Software Reuse. In: MEDINFO ’98; 1998
42 Smith DS, Park JY, Musen MA. Therapy Planning as Constraint Satisfaction: A Computer-Based Antiretroviral Therapy Advisor for the Management of HIV. In: AMIA Fall Symposium; 1998
43 Gennari JH, Tu SW, Rothenfluh TE, Musen MA. Mapping Domains to Methods in Support of Reuse. Int J Human-Computer Studies 1994; 41: 399-424.
44 Park JY, Gennari JH, Musen MA. Mappings for Reuse in Knowledge-Based System. In: 11th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff, Alberta, Canada: 1998
45 Fensel D, Motta E. Structured Development of Protein Solving Methods. In: 11th Banff Knowledge Acquisition for Knowledge-Based Systems Workshop. Banff, Alberta, Canada: 1998
46 Krueger CW. Software Reuse. ACM Computing Surveys 1992; 24: 131-83.
47 Musen MA. Dimensions of Knowledge Sharing and Reuse. Comput Biomed Res 1992; 25: 435-67.
48 Gamma E, Helm R, Johnson R, Vlissides J. Design Patterns: Elements of Reusable Object-Oriented Software. Reading, MA: Addison-Wesley Publishing Company; 1995
49 Tu SW, Musen MA. Episodic Refinement of Episodic Skeletal Plan Refinement. Int J Human-Computer Studies 1998; 48: 475-97.
50 van Bemmel JH. Medical Informatics: Art or Science?. Meth Inform Med 1996; 35: 157-72.
51 Rindfleisch TR. (Bio)medical Informatics in the Next Decade. J Am Med Inform Assoc 1998; 5: 416-20.
52 Musen MA. Design and Use of Clinical Ontologies: Curricular Goals for the Education of Health-Telematics Professionals. In: International Workshop on User Acceptance of Telematics Applications by Health-care Professionals. Thessaloniki, Greece: 1998
53 Musen MA. Modern Architectures for Intelligent Systems: Reusable Ontologies and Problem-Solving Methods. In: AMIA 98 Annual Symposium. Orlando, FL: Hanley & Belfus, Inc; 1998