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Knowledge engineering for clinical consultation programs: modeling the application area.

M A Musen, J van der Lei

    Methods of Information in Medicine
    |January 1, 1989
    PubMed
    Summary
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    Computer-based decision-support tools face challenges in creating computational models, regardless of using artificial intelligence or pattern recognition. Both fields must unite to overcome knowledge acquisition and brittleness issues in developing complex clinical consultation programs.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Medical Informatics

    Background:

    • Decision-support tools often use artificial intelligence (AI) or pattern recognition (PR) techniques.
    • Developers frequently emphasize differences between AI and PR, overlooking fundamental similarities.
    • Creating computational models for clinical consultation programs presents significant challenges.

    Purpose of the Study:

    • To explore the fundamental similarities between AI and PR in developing decision-support tools.
    • To analyze the challenges of knowledge acquisition and brittleness in both AI and PR approaches.
    • To advocate for collaboration between AI and PR communities to address shared problems.

    Main Methods:

    • Comparative analysis of AI and PR methodologies in decision-support systems.

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  • Exploration of the "knowledge-acquisition bottleneck" and "brittleness problem" within both AI and PR contexts.
  • Literature review and conceptual framework development.

    • Main Results:

    • The core challenge in developing clinical consultation programs is creating accurate computational models.
    • Both AI (expert systems) and PR face analogous difficulties, including knowledge acquisition and brittleness.
    • Fundamental similarities in challenges exist between AI and PR approaches.

    Conclusions:

    • The AI and PR communities share common obstacles in model development.
    • Collaboration is essential to develop unified methods for building complex application models.
    • Addressing the knowledge-acquisition bottleneck and brittleness requires a joint effort.