A computational model of reasoning from the clinical literature

Bibliographic Information

A computational model of reasoning from the clinical literature

Glenn D. Rennels

(Lecture notes in medical informatics, v. 32)

Springer-Verlag, 1987

Available at  / 1 libraries

Search this Book/Journal

Note

Bibliography: p. [213]-228

Includes index

Description and Table of Contents

Description

As research on expert systems has moved well into its second decade, it has become popular to cite the limitations of the phenomenologic or associational approach to knowledge representation that was typical of first generation systems. For example, the Internist-1 knowledge base represents explicitly over 600 diseases, encoding associated disease manifestations (signs, symptoms, physical findings, and lab abnormalities) but failing to deal with the reasons that those findings may be present in the disease [Miller, R. A. 82]. In recent years Pople has sought to add detailed causal models to the knowledge base in a revised version of the program known as CADUCEUS [Pople 82]. Similarly, a typical production rule in the MYCIN system states inferences that may be drawn when specific conditions are found to be true [Buchanan 84], but the underlying explanations for such relationships are not encoded. Clancey has argued that MYCIN needs such "supporting knowledge" represented, especially if its knowledge base is to be used for teaching purposes [Clancey 83]. By the late 1970s, artificial intelligence researchers were beginning to experiment with reasoning systems that used detailed mechanistic or causal niodels of the object being analyzed. Among the best early examples were a program to teach students how to analyze electronic circuits [Brown 82] and a system for diagnosing problems with mechanical devices [Rieger 76].

Table of Contents

1. Introduction.- 1.1. Introduction to the Problem.- 1.2. Research Themes.- 1.3. The Roundsman System: Examples.- 1.4. Research Contributions.- 1.5. Guide to the Reader.- 2. Background.- 2.1. A Review of Artificial Intelligence in Medicine.- 2.1.1. Introduction.- 2.1.2. Theoretical Basis.- 2.1.3. Research Themes.- 2.1.4. Summary of Roundsman's Relationship to Prior AIM Research.- 2.2. Consultation Systems for Biostatistics.- 2.3. Computer Databases.- 2.3.1. Bibliographic Retrieval Systems.- 2.3.2. Databases of Patient Records.- 2.4. Decision Analysis.- 2.5. Scope of the Biomedical Reports in Roundsman's Library.- 2.6. Guides to Clinical Application of Biomedical Reports.- 2.7. Management of Primary Breast Cancer.- 2.7.1. Treatment Options.- 2.7.2. Treatment Objectives.- 2.7.3. Controversy.- 3. Overview of the Research and the Roundsman System.- 3.1. Informal Protocol Analysis.- 3.2. The Development of Target Scripts.- 3.3. Selection of Publications to Include in Roundsman.- 3.4. Hardware and Software Support.- 3.5. An Overview of the Roundsman System.- 4. Modeling Distance from Study to Decision.- 4.1. Representing a Study's Basic Statistical Results.- 4.2. A Taxonomy of Distance.- 4.3. Calibration of Distance.- 4.4. Distance Estimators.- 4.5. Work Related to Roundsman's Distance Metric.- 4.6. Summary.- 5. Choice and Explanation.- 5.1. Introduction.- 5.2. Multiattribute Decision Making.- 5.3. The Importance of Modelling Both Choice and Explanation.- 5.4. Four Strategies for Choice and Explanation.- 5.5. Lexicographical Ordering.- 5.5.1. Operational Definition.- 5.5.2. Examples.- 5.5.3. Assumptions.- 5.5.4. Explanation.- 5.6. Satisficing.- 5.6.1. Operational Definition.- 5.6.2. Examples.- 5.6.3. Assumptions.- 5.6.4. Explanation.- 5.7. Dominance.- 5.7.1. Operational Definition.- 5.7.2. Examples.- 5.7.3. Assumptions.- 5.7.4. Explanation.- 5.8. Trade-Off.- 5.8.1. Operational Definition.- 5.8.2. Examples.- 5.8.3. Assumptions.- 5.8.4. Explanation.- 5.9. Comparison Strategies Under Uncertainty.- 5.10. Implementation in Roundsman.- 5.11. Summary.- 6. Interactions Between Studies.- 6.1. Updating Roundsman Over Time.- 6.2. Conflict Between Study Results.- 6.2.1. Bayes' Estimates and Meta-Analysis.- 6.2.2. Explicit Use of Inter-Study Domain Knowledge.- 6.3. Summary and Future Directions.- 7. Text Generation.- 7.1. Introduction.- 7.2. Examples.- 7.3. Programming Language Primitives: Objects and Messages.- 7.4. Overview of the Implementation.- 7.5. Operational Description of the Objects.- 7.5.1. Prose Fact.- 7.5.2. Start-State.- 7.5.3. Pop-State.- 7.5.4. Cond-State.- 7.5.5. Option-State.- 7.5.6. Sequence-State.- 7.5.7. Push-Arc.- 7.5.8. Jump-Arc.- 7.5.9. Text-Arc.- 7.5.10. Endsequence-Arc.- 7.5.11. Function-Arc.- 7.6. Discussion.- 8. Extended Examples and Evaluation.- 8.1. Example 1.- 8.2. Example 2.- 8.3. Example 3.- 8.4. Example 4.- 8.5. Example 5.- 8.6. Example 6.- 8.7. Example 7.- 8.8. Example 8.- 8.9. Evaluation.- 8.9.1. Introduction.- 8.9.2. Methods.- 8.9.3. Results.- 8.9.4. Discussion.- 9. Conclusions.- 9.1. Summary.- 9.2. Limitations.- 9.3. Promising Areas for Extending the Research.- Appendix I. Internal Representation of the Studies in Roundsman's Library.- Appendix II. Sample Distance Estimators.- References.

by "Nielsen BookData"

Related Books: 1-1 of 1

Details

  • NCID
    BA23204400
  • ISBN
    • 3540179496
  • LCCN
    87004895
  • Country Code
    gw
  • Title Language Code
    eng
  • Place of Publication
    Berlin ; New York
  • Pages/Volumes
    xi, 230 p.
  • Size
    25 cm
  • Parent Bibliography ID
Page Top