Causal Representation of Patient Illness
Electrolyte and Acid-Base Diagnosis


Ramesh S. Patil

October 1981

Technical Report

Laboratory for Computer Science

Cambridge Massachusetts 02139

Massachusetts Institute of Technology

This research was supported (in part) by the National Institutes of Health Grant No. 1 P01 LM 03374-Ox from the National Library of Medicine.

Submitted to the Department of Electrical Engineering and Computer Science on October 24,1981 in partial fulfillment of the requirements for the Degree of Doctor of Philosophy


Much of the medical knowledge in the first generation Al in Medicine programs is phenomenological; that is, it describes the associations among phenomena without knowledge of the underlying causal mechanisms. Although these AIM programs provide a good first approximation to the way clinicians reason, they fail to reproduce clinicians' reasoning based on a deeper understanding of the phenomena. More specifically, they do not deal with the knowledge of disease at different levels of detail, nor do they utilize causal relations to organize and explain the clinical facts and disease hypotheses. They also cannot deal with illnesses resulting from multiple diseases, especially when one disease alters the presentation of the others. Finally, they are unable to capture the notions of adequacy and parsimony that play such a large role in diagnosis. To explore these issues and rectify these deficiencies, we have undertaken the task of providing expert consultation for electrolyte and acid-base disturbances.

This thesis reports the implementation of ABEL, the diagnostic component of the consultation program. In it, we explore the problems of modeling the causal understanding of a patient's illness. We develop techniques for dealing with illness resulting from multiple interacting diseases. We describe a multi-level representation of causal knowledge, and explore issues of the aggregation of available case specific knowledge into concise summaries of the patient's illness. We discuss structural criteria for evaluating parsimony, coherence and adequacy of diagnostic explanations. We also explore some of the issues involved in information gathering and propose expectation-driven diagnostic planning as a means of improving it. Finally, we discuss the issues of explanation and justification of the program's understanding and argue that these facilities are crucial for acceptability of a consultation program.

Thesis supervisor: Peter Szolovits
Associate Professor of Electrical Engineering and Computer Science

Keywords: Causal Representation, Medical Diagnosis, Structure Building Operations


1. Introduction8
    1.1 Scope of Project12
    1.2 Choice of Domain14
    1.3 Brief review of Electrolyte and Acid-Base Disorders 15
    1.4 Desiderata19
        1.4.1 Making a Correct Diagnosis 19
        1.4.2 Continued Management of the Patient 19
        1.4.3 Diagnostic Style 19
        1.4.4 Mode of Interaction 20
        1.4.5 Handling Discrepant Information 20
    1.4.6 Explanation21
    1.5 Survey of AIM programs22
        1.5.1 Internist-I and Present Illness Program 23
        1.5.2 CASNET/Glaucoma 25
        1.5.3 Mycin 25
    1.6 Outline of the Thesis27
2. Examples30
    2.1 Example 1: Salmonellosis30
    2.2 Example 2: Vomiting and Salmonellosis 40
3. Representation of Medical Knowledge43
    3.1 Anatomical Knowledge44
        3.1.1 Anatomical Taxonomy 44
        3.1.2 Material Flow Pathways 44
        3.1.3 Anatomical Spaces 46
        3.1.4 Miscellaneous Gross Anatomical Relations 48
    3.2 Etiological Knowledge49
    3.3 Physiological Knowledge50
    3.4 Disease Knowledge51
    3.5Causal Link53
    3.6 Multi-Level Causal Description 54
4. Structure Building Operations61
    4.1 Structure of a PSM61
    4.2 Initial Formulation62
    4.3 Some Definitions63
    4.4 Aggregation65
        4.4.1 Focal Aggregation 65
        4.4.2 Causal Aggregation 66
    4.5 Elaboration69
        4.5.1 Focal Elaboration 69
        4.5.2 Causal Elaboration 70
    4.6 Projection72
    4.7 Component Summation and Decomposition 72
5. Diagnostic Problem Formulation and Information Gathering 80
    5.1 Global Diagnostic Cycle82
    5.2 Diagnostic Closure of a Hypothesis 83
    5.3 Scoring the PSM85
    5.4 Scoring a Disease Hypothesis 85
    5.5 Information Gathering Strategy 88
6. Examples Revisited95
7. Conclusion118
    7.1 Summary118
    7.2 Limitations of ABEL and Future Directions 119
8. References122
Appendix I. System Building Tool: XLMS130
    I.1 XLMS Concepts130
    I.2 The XLMS Interpreter133
Appendix II. Explanation134
    II.1 Phrase Generator135
    II.2 Higher level explanations135
    II.3 Organizing causal Explanation 136


Fig. 1. A schematic for the overall system 13
Fig. 2. Carbonic acid - bicarbonate buffer equation 17
Fig. 3. Nomogram of acid-base disturbances 18
Fig. 4. Graphic depiction of the two Acid-Base hypotheses 31
Fig. 5. Comparison of hypotheses 1 & 2 at clinical level 33
Fig. 6. Comparison of hypotheses 1 & 2 at intermediate level 34
Fig. 7. The part-of hierarchy45
Fig. 8. Material flow relations46
Fig. 9. The containment relation47
Fig. 10. Gross anatomical relations48
Fig. 11. Etiological hierarchy49
Fig. 12. Schematic description of a causal link 53
Fig. 13. Schematic description of the node structure 55
Fig. 14. Comparison of lower Gi fluid and of plasma 56
Fig. 15. The loss of electrolytes in lower Gi fluid 56
Fig. 16. Consequences of lower Gi loss described at next higher level 57
Fig. 17. Lower Gi loss expressed at an intermediate level 57
Fig. 18. Salmonellosis and its consequences expressed at the clinical level 58
Fig. 19. Layered description of link: salmonellosis causes dehydration 58
Fig. 20. Compiled link59
Fig. 21. Node types64
Fig. 22. Causal aggregation: fully Unaccounted node 66
Fig. 23. Causal aggregation: fully accounted node 67
Fig. 24. Causal aggregation: partially accounted node 68
Fig. 25. An example of the elaboration process 71
Fig. 26. An example of component summation/decomposition 73
Fig. 27. Feedback loop represented using component summation 74
Fig. 28. Component summation/decomposition: Case 3 77
Fig. 28. (continued)78
Fig. 29. An example of diagnostic closure 84
Fig. 30. An example of explained, unexplained and unaccounted findings 86
Fig. 31. Initial diagnostic closure for salmonellosis and acute renal failure 89
Fig. 32. Diagnostic closure separated for each possibility 90
Fig. 33. Diagnostic closures for each possibility projected forward 91
Fig. 34. The goal tree92
Fig. 35. Serum electrolytes and the bar diagram 95
Fig. 36. Graphical description of acid-base disturbances 96
Fig. 37. Hypothesis 198
Fig. 38. Hypothesis 299
Fig. 39. Aggregation of low-serum-K-1100
Fig. 40. Aggregation of low-pH-1100
Fig. 41. PSM for hypothesis 1101
Fig. 42. PSM for hypothesis 2102
Fig. 43. Diagnostic closure 1103
Fig. 44. Diagnostic closure 2104
Fig. 45. One complete cycle of diagnostic inquiry 105
Fig. 46. Diagnostic closure 3106
Fig. 47. Diagnostic closure 4107
Fig. 48. Diagnostic closure 5107
Fig. 49. After all findings have been exhausted 108
Fig. 50. Hypothesis 1 with salmonellosis 109
Fig. 51. Hypothesis 2 with salmonellosis 110
Fig. 52. English description of the two hypotheses 112
Fig. 53. Initial PSM114
Fig. 54. Revised PSM after vomiting is entered 115
Fig. 55. Final PSM after salmonellosis is introduced 116
Fig. 56. English text of the final explanation 117
Fig. 57. The XLMS hierarchy131
Fig. 58. Feedback relation betweeen acidemia and hypocapnia 137


I would like to express my thanks to all of the people who made this thesis possible:

Peter Szolovits, my thesis supervisor, for providing constant attention, guidance and for helping me formalize ideas when I could not see through the confusion;

William B. Schwartz for suggesting this thesis topic, teaching me about acid-base and electrolyte disturbances, and for articulating and refining many of the ideas presented in this thesis;

Randall Davis, for his helpful comments and suggestions on drafts of this document;

William Martin for introducing me to the area of knowledge based application systems and providing the intellectual environment in which these ideas germinated;

Lowell Hawkinson for developing XLMS and providing help in using it;

Bill Long for his encouragement and for being a very insightful sounding-board for ideas;

Glenn Burke for proofreading this document and for providing much needed system support;

Ken Church for many spirited arguments and discussions on this and other topics;

Bill Swartout for timely development of explanation methodology and for sharing his experience in using XLMS;

Stephen Pauker, Brian Smith, Ben Kuipers, Byron Davies, Howard Sherman, Harold Goldberger, Brij Masand, Gretchen Brown and other past and present members of Clinical Decision-Making group and Knowledge-Base System groups for providing the fertile environment and comradery needed to carry me through the thesis;

and finally, my wife Aruna and my family for bearing with me and providing constant encouragement without which this would not have been possible.

... on to Chapter 1

This document is a Web-accessible version of
Patil, Ramesh S. Causal Representation of Patient Illness for Electrolyte and Acid-Base Diagnosis. MIT Lab for Computer Science Technical Report TR-267. October 1981. Also: Ph.D. Thesis, MIT Dept. of Electrical Engineering and Computer Science.
The document was reconstructed for the Web in April 2002 by Peter Szolovits. It is also available as a scanned (bitmapped) pdf document.  Page numbers in the Contents and Table of Figures refer to this original pagination. You might prefer that version for printing.