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Knowledge bases in medicine: a review.

C A Perry1

  • 1Reference Department, New York Academy of Medicine Library, New York 10029.

Bulletin of the Medical Library Association
|July 1, 1990
PubMed
Summary

This review explores medical knowledge representation systems, from electronic textbooks to expert systems. It highlights challenges in knowledge acquisition and representation, offering solutions for effective medical informatics.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Effective knowledge representation is crucial for medical informatics advancement.
  • Medical knowledge bases range from simple electronic textbooks to complex expert systems.

Purpose of the Study:

  • To review representative medical knowledge bases and knowledge-based systems.
  • To describe relationships among different approaches and identify future trends.
  • To examine challenges in knowledge representation and acquisition.

Main Methods:

  • Review of electronic textbooks (PDQ, Hepatitis Knowledge Base).
  • Analysis of rule-based systems (MYCIN), causal models (CASNET), and frame-based systems (PIP, INTERNIST-1).
  • Examination of knowledge acquisition and representation challenges and solutions.

Main Results:

  • Diverse approaches exist, including electronic textbooks, rule-based, causal, and frame-based systems.
  • Key challenges involve knowledge representation and acquisition.
  • Solutions include using software shells, adapting existing knowledge bases, and enabling multiple uses.

Conclusions:

  • Medical knowledge representation is a dynamic field with various system types.
  • Addressing knowledge acquisition and representation challenges is vital for system development.
  • Future trends involve reusable knowledge bases and domain-independent tools.

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