Pattern-Based Interactive Diagnosis of Multiple Disorders: The Medas System

Moshe Ben-Bassat, Richard W. Carlson, Venod K. Puri, Mark D. Davenport, John A. Schriver, Mohamed Latif, Ronald Smith, Larry D. Portigal, Edward H. Lipnick, Max Harry Weil

Research output: Contribution to journalArticlepeer-review

71 Scopus citations

Abstract

A knowledge-based interactive sequential diagnostic system is introduced which provides for diagnosis of multiple disorders in several body systems. The knowledge base consists of disorder patterns in a hierarchical structure that constitute the background medical information required for diagnosis in the domain under consideration (emergency and critical care medicine, in our case). Utilizing this knowledge base, the diagnostic process is driven by a multimembership classification algorithm for diagnostic assessment as well as for information acquisition [1]. A key characteristic of the system is congenial man-machine interface which comes to expression in, for instance, the flexibility it offers to the user in controlling its operation. At any stage of the diagnostic process the user may decide on an operation strategy that varies from full user control, through mixed initiative to full system control. Likewise, the system is capable of explaining to the user the reasoning process for its decisions. The model is independent of the knowledge base, thereby permitting continuous update of the knowledge base, as well as expansions to include disorders from other disciplines. The information structure lends itself to compact storage and provides for efficient computation. Presently, the system contains 53 high-level disorders which are diagnosed by means of 587 medical findings.

Original languageEnglish
Pages (from-to)148-160
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
VolumePAMI-2
Issue number2
DOIs
StatePublished - Mar 1980
Externally publishedYes

Keywords

  • Artificial intelligence
  • Bayesian classification
  • medical diagnosis
  • multimembership classification
  • pattern recognition
  • pattern-based systems

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