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Related Experiment Videos

A prototype system for perinatal knowledge engineering using an artificial intelligence tool.

R J Sokol1, L Chik

  • 1Department of Obstetrics and Gynecology, Wayne State University/Hutzel Hospital, Detroit, Michigan.

Journal of Perinatal Medicine
|January 1, 1988
PubMed
Summary
This summary is machine-generated.

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Artificial intelligence (AI) shows promise for developing perinatal expert systems. A semiautomated method achieved 79% sensitivity in identifying key facts from high-risk pregnancy literature for knowledge base creation.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Perinatal Medicine

Background:

  • Despite existing perinatal expert systems, artificial intelligence (AI) has had limited impact on medical computing.
  • Developing comprehensive perinatal knowledge bases remains a challenge.

Purpose of the Study:

  • To evaluate the potential of AI techniques for creating a computer-based "Perinatal Consultant."
  • To develop a perinatal knowledge base using a "top-down" approach from existing literature.

Main Methods:

  • Utilized a "top-down" strategy to build a perinatal knowledge base from a high-risk pregnancy manuscript.
  • Employed the UNIX utility "style" for natural language processing to extract keywords and phrases.
  • Implemented a semiautomated method with a nonmedical speller to identify key perinatal concepts.

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Main Results:

  • The semiautomated method achieved a 79% sensitivity in detecting key sentences containing essential facts.
  • This indicates that approximately 8 out of 10 critical sentences were identified for knowledge base construction.
  • The approach successfully identified key perinatal concepts for AI application.

Conclusions:

  • Encouraging results suggest AI can expedite the development of functional perinatal expert systems.
  • Combining programming utilities with AI tools and medical literature is a viable strategy.
  • This approach facilitates the creation of robust knowledge bases for clinical decision support.