Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations,

Abstract

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1981
Accession Number
ADA112709

Entities

People

  • Edward H. Shortliffe
  • Jerold W. Wallis

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Acquisition
  • Amniotic Fluid
  • Artificial Intelligence
  • Causal Reasoning
  • Computer Science
  • Computers
  • Diseases And Disorders
  • Endocrine System
  • Expert Systems
  • Infection
  • Internal Medicine
  • Language
  • Medical Personnel
  • Metabolic Diseases
  • Probability
  • Reasoning
  • Wound Infections

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval