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Scalable software architectures for decision support.

M A Musen1

  • 1Stanford Medical Informatics, Stanford University School of Medicine, CA, USA. Musen@Stanford.EDU

Methods of Information in Medicine
|May 11, 2000
PubMed
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Decision-support systems in medicine are evolving. Reusable components like domain ontologies and problem-solving methods offer a path to creating large, maintainable clinical decision support systems.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Interest in clinical decision-support programs surged in the 1970s, with a focus on rule-based systems.
  • Development of large rule bases for clinical decision support proved challenging due to creation and maintenance difficulties.

Purpose of the Study:

  • To highlight how domain ontologies and problem-solving methods can be used to build large, maintainable decision-support systems.
  • To emphasize the importance of domain ontologies and problem-solving methods as fundamental products of medical informatics research.

Main Methods:

  • Exploration of alternative programming abstractions beyond rule-based systems.
  • Development and utilization of reusable components: domain-independent algorithms (problem-solving methods) and domain ontologies.

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Main Results:

  • The influential notions of "generic tasks" and reusable problem-solving methods emerged in the 1980s.
  • Academic centers experimented with intelligent system architectures based on reusable components by the 1990s.

Conclusions:

  • Domain ontologies and problem-solving methods are key building blocks for constructing large, maintainable decision-support systems.
  • These concepts represent fundamental outcomes of medical informatics research and warrant greater attention from the scientific community.