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Loading a nursing expert system from text: a case study

W T Harding1, R T Redmond, M C Corley

  • 1College of Business Administration, Texas A & M University-Corpus Christi 78412.

Computers in Nursing
|November 1, 1993
PubMed
Summary
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This study introduces a novel model for expert systems, utilizing text as a knowledge source to overcome traditional expert elicitation bottlenecks. Techniques are presented to extract and formalize knowledge from unstructured text for expert system development.

Area of Science:

  • Artificial Intelligence
  • Knowledge Engineering
  • Medical Informatics

Background:

  • Expert system construction faces challenges in acquiring and formalizing human expertise.
  • Existing methods struggle with knowledge extraction from unstructured text formats.

Purpose of the Study:

  • To propose and validate a model for building expert systems using text as the primary knowledge source.
  • To address the bottleneck of expert knowledge acquisition through automated text processing.

Main Methods:

  • Developed techniques for knowledge extraction and formalization from textual data.
  • Implemented a case study to build a nurse expert system using the proposed model.
  • Focused on replicating the diagnostic reasoning of professional nurses.

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Main Results:

  • Demonstrated the feasibility of using text as a direct knowledge source for expert systems.
  • Successfully built a nurse expert system capable of medical diagnostic activities.
  • Validated the proposed model's effectiveness in knowledge acquisition from text.

Conclusions:

  • Text-based knowledge acquisition offers a viable alternative to traditional expert elicitation.
  • The proposed model provides a framework for developing expert systems from readily available textual information.
  • This approach can significantly reduce the bottleneck in expert system development.