Computer Assisted Medical Diagnosis Problems and Methods to Minimize Their Effects.

Abstract

The background and methods used in Computer Assisted Medical Diagnosis (CAMD) are discussed and the problems and errors encountered during the development of CAMD systems are examined. Five specific methods and algorithms are discussed in terms of the problems previously reported in the literature, as well as, possible solutions and ways to minimize these problems. The use of multiple experts, adequate sampling sizes, multicenter samples with adequate and representative disease cases in the development of CAMD systems is recommended. Limitations of these systems can be determined from thorough system validation and testing of disease cases and sign/ symptom data that were not used in the initial system development. The utilization of sign and symptom disease complexes (indice, scales), and the use of multiple disease diagnostic methods to identify possible disease diagnosis is emphasized. Furthermore, CAMD systems should give standard definitions and interpretations for any signs, symptoms, lab results, diseases, or treatments suggested, and at a time the level of diagnostic expertise of the user should be considered. The development of the CAMD systems using a data base management approach that allow the integration of multiple methods is discussed as a valuable approach for implementing systems. Computer Assisted Medical Diagnosis, diagnostic algorithms Expert systems, Rule-based, Neural Network.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1991
Accession Number
ADA258367

Entities

People

  • David H. Ryman

Organizations

  • Naval Health Research Center

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Biomedical Research
  • Computational Science
  • Computer Programs
  • Computers
  • Databases
  • Disease Attributes
  • Expert Systems
  • Health Services
  • Medical Personnel
  • Neural Networks
  • Pain
  • Reasoning
  • Signs And Symptoms
  • Standards

Fields of Study

  • Medicine

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Gulf War Illness and Chronic Multisymptom Illness in Veterans.
  • Instructional Design and Training Evaluation.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference