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 it 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)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1982
Accession Number
ADA120936

Entities

People

  • Edward H. Shortliffe
  • Jerold W. Wallis

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Classification
  • Demographic Cohorts
  • Expert Systems
  • Prototypes
  • Reasoning
  • Refining

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval