Methods Inf Med 1987; 26(03): 78-88
DOI: 10.1055/s-0038-1635497
Original Article
Schattauer GmbH

Graphical Access to Medical Expert Systems: III. Design of a Knowledge Acquisition Environment*)

Grafischer Zugriff zu medizinischen Expertensystemen: III. Entwurf einer Wissenserfassungs-Stelle
Joan D. Walton
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
,
M. A. Musen
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
,
D. M. Combs
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
,
C. D. Lane
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
,
E. H. Shortliffe
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
,
L. M. Fagan
1   (From the Medical Computer Science Group, Knowledge Systems Laboratory, Departments of Medicine and Computer Science, Stanford University School of Medicine)
› Author Affiliations

his work has been supported by the National Library of Medicine under Grants LM-04420, LM-07033, and LM-04316, and by the Division of Research Resources under Grant RR-01631. Dr. Musen and Dr. Shortliffe have received support from the Henry J. Kaiser Family Foundation. Computer facilities were provided by the SUMEX-AIM resource under Grant RR-00785 and through an equipment gift from Xerox Corporation.
Further Information

Publication History

Publication Date:
16 February 2018 (online)

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Summary

Knowledge acquisition for expert systems typically is a tedious, iterative process involving long hours of consultation between the domain experts and the computer scientists who serve as knowledge engineers. For well-understood domains, however, it may be possible to facilitate the knowledge acquisition process by allowing domain experts to develop and edit a knowledge base directly. Administration of protocol-directed cancer chemotherapy is such a well-understood application area, and a knowledge acquisition system, called OPAL, has been developed for eliciting chemotherapy-protocol knowledge directly from expert oncologists. OPAL’s knowledge acquisition approach is based on the interactive graphics environment available on current generation workstations. The use of graphics improves the interface by reducing typing, avoiding natural language interpretations, and allowing flexibility in entry sequence. The knowledge in OPAL is displayed using an arrangement of hierarchically related, graphical forms. The position of a particular form in the hierarchy defines the context of the knowledge contained in the form. Intelligent editing programs such as OPAL can streamline the knowledge engineering process for highly structured domains requiring repetitive knowledge entry.

Wissenserfassung für Expertensysteme ist immer ein mühsamer, iterativer Prozeß, der stundenlange Konsultationen zwischen den Gebietsexperten und den Computerwissenschaft-lern, die als Wissensingenieure agieren, mit sich bringt. Auf bestimmten Gebieten kann es jedoch möglich sein, den Wissenserfassungsprozeß zu erleichtern, indem man Gebietsexperten gestattet, eine Wissensbank unmittelbar zu entwickeln und zu bearbeiten. Die Verwaltung einer protokollorientierten Krebschemotherapie ist ein solches fest umgrenztes Anwendungsgebiet, und ein Wissenserfassungssystem, OPAL genannt, wurde entwickelt, um Wissen über das Chemotherapieprotokoll unmittelbar von Experten aus der Onkologie zu erfahren. Die Methode der Wissenserfassung in OPAL basiert auf den interaktiven grafischen Möglichkeiten, die bei derzeitigen Arbeitsstationen gegeben sind. Der Gebrauch der Grafik verbessert das Interface, indem er Schreibarbeit reduziert, Interpretationen natürlicher Sprache vermeidet und Flexibilität in der Eingabesequenz ermöglichen. Das bekannte Wissen wird in OPAL unter Benutzung einer Anordnung hierarchisch verwandter grafischer Formen dargeboten. Die Stellung einer bestimmten Form in der Hierarchie definiert den Kontext des in der Form enthaltenen Wissens. Intelligente Ausgabeprogramme wie OPAL können den Prozeß der Wissensbearbeitung für hochstrukturierte Gebiete, die wiederholte Wissenseingabe erfordern, den Gegebenheiten anpassen.