Download PDF
Methods Inf Med 1991; 30(04): 241-255
DOI: 10.1055/s-0038-1634846
Original Article
Schattauer GmbH

Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base

I. The Probabilistic Model and Inference Algorithms
M. A. Shwe
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
B. Middleton
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
D. E. Heckerman
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
M. Henrion
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
E. J. Horvitz
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
H. P. Lehmann
1   Section on Medical Informatics, Stanford University, Stanford, CA
,
G. F. Cooper
*   Section of Medical Informatics, University of Pittsburgh, Pittsburgh, PA, U.S.A
1   Section on Medical Informatics, Stanford University, Stanford, CA
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)

    Permissions and Reprints

Abstract:

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics.

We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Key-Words

Expert Systems - Computer-aided Diagnosis - Probabilistic Inference - Belief Networks

® QMR is a registered trademark of the University of Pittsburgh.


 

  • REFERENCES

  • 1 Miller RA, Pople HEJ, Myers JD. Internist-1: An experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 1982; 307: 468-76.
    CrossrefPubMedGoogle Scholar
  • 2 Miller RA. INTERNIST-1/CADUCEUS: Problems facing expert consultant programs. Meth Inform Med 1984; 23: 9-14.
    PubMedGoogle Scholar
  • 3 Miller RA, McNeil MA, Challinor SM, Masarie FEJ, Myers JD. The INTERNIST-1/QUlCK MEDICAL REFERENCE project – Status report. West J Med 1986; 145: 816-22.
    PubMedGoogle Scholar
  • 4 Miller R, Masarie FE, Myers JD. Quick medical reference (QMR) for diagnostic assistance. MD Computing 1986; 03: 34-48.
    PubMedGoogle Scholar
  • 5 First MB, Softer LJ, Miller RA. QUICK (Quick Index to Caduceus Knowledge): Using the INTERNIST-1/CADUCEUS knowledge base as an electronic textbook of medicine. Comp Biomed Res 1985; 18: 137-65.
    CrossrefPubMedGoogle Scholar
  • 6 Miller RAEMF. Use of the Quick Medical Reference program as a tool for medical education. Meth Inform Med 1989; 28: 340-5.
    PubMedGoogle Scholar
  • 7 Von Neumann J, Morgenstern O. Theory of Games and Economic Behavior, Princeton. New York: Princeton Univ Press; 1947
    Google Scholar
  • 8 Spiegelhalter DJ, Franklin RCG, Bull K. Assessment, criticism and improvement of imprecise subjective probabilities for a medical expert system. In: Machine Intelligence and Pattern Recognition: Uncertainty in Artificial Intelligence 5. Henrion M, Shachter R, Kanal LN, Lemmer JF. eds. Amsterdam: North-Holland Publ Comp; 1990: 285-94.
    Google Scholar
  • 9 Harris N, Spiegelhalter DJ, Bull K, Franklin RC. Criticizing conditional probabilities in belief networks. In: Proceedings of the Fourteenth Annual Symposium on Computer Applications in Medical Care. Miller RA. ed. Los Alamitos, CA: IEEE Comp Soc Press; 1990: 805-9.
    Google Scholar
  • 10 Masarie Jr FE, Miller RA, Myers JD. INTERNIST-1 properties: Representing common sense and good medical practice in a computerized medical knowledge base. Comp Biomed Res 1985; 18: 458-79.
    CrossrefPubMedGoogle Scholar
  • 11 Heckerman DE, Horvitz EJ, Nathwani BN. Update on the Pathfinder project. In: Proceedings of the Thirteenth Annual Symposium on Computer Applications in Medical Care. Kingsland LC. ed. Los Alamitos, CA: IEEE Comp Soc Press; 1989: 203-7.
    Google Scholar
  • 12 deDombal FT, Leaper DJ, Horrocks JC, Staniland JR, McCain AP. Human and computer-aided diagnosis of abdominal pain: Further report with emphasis on performance. Brit Med J 1974; 01: 376-80.
    CrossrefPubMedGoogle Scholar
  • 13 Bankowitz RA, McNeil MA, Challinor SM, Parker RC, Kapoor WN, Miller RA. A computer-assisted medical diagnostic consultation service: Implementation and prospective evaluation of a prototype. Ann Inter Med 1989; 110: 824-32.
    CrossrefPubMedGoogle Scholar
  • 14 Cooper GF. Current research directions in the development of expert systems based on belief networks. Appl Stoch Models Data Anal 1989; 05: 39-52.
    CrossrefPubMedGoogle Scholar
  • 15 Horvitz EJ, Breese JS, Henrion M. Decision theory in expert systems and artificial intelligence. J Approx Reas 1988; 02: 247-302.
    PubMedGoogle Scholar
  • 16 Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufman; 1988
    Google Scholar
  • 17 Howard RA, Matheson JE. Influence diagrams. In: Readings on the Principles and Applications of Decision Analysis. Howard RA, Matheson JE. eds. Menlo Park, CA: Strategic Decisions Group; 1981: 721-62.
    Google Scholar
  • 18 Chavez RM, Cooper GF. Hypermedia and randomized algorithms for medical expert systems. Comp Meth Progr Biomed 1990; 32: 5-16.
    CrossrefPubMedGoogle Scholar
  • 19 Cooper GF. NESTOR: A Computer-Based Medical Diagnostic Aid That Integrates Causal and Probabilistic Knowledge. (Ph. D. Diss). Medical Information Sciences. Stanford, CA: Stanford University; 1984
    Google Scholar
  • 20 Andersen SK, Olesen KG, Jensen FV, Jensen F. HUGIN – A shell for building Bayesian belief universes for expert systems. In: Eleventh International Joint Conference on Artificial Intelligence. Sridharan NS. ed. San Mateo, CA: Morgan Kaufmann Publ; 1989: 1080-5.
    Google Scholar
  • 21 Heckerman DE. A tractable inference algorithm for diagnosing multiple diseases. In: Machine Intelligence and Pattern Recognition: Uncertainty in Artificial Intelligence 5. Henrion M, Shachter R, Kanal LN, Lemmer JF. eds. Amsterdam: North-Holland Publ Comp; 1990: 163-72.
    Google Scholar
  • 22 Henrion M. Towards efficient probabilistic diagnosis in multiply connected networks. In: Influence Diagrams, Belief Nets and Decision Analysis. Oliver RM, Smith JQ. eds. Chichester: Wiley; 1990: 385-407.
    Google Scholar
  • 23 Peng Y, Reggia JA. A probabilistic causal model for diagnostic problem solving – part I: Integrating symbolic causal inference with numeric probabilistic inference. IEEE Trans Syst Man, and Cybern. 1987. SMC-17: 146-62.
    Google Scholar
  • 24 Ganote DP. A Bayesian Set Covering Model Applied to Diagnosis in Blood Typing. (M. Sc. Thesis). Department of Computer Science. University of Ohio; 1989
    Google Scholar
  • 25 Habbema JDF. Models for diagnosis and detection of combinations of diseases. In: Decision Making and Medical Care. De Dombal FT, Gremy F. eds. New York: North-Holland Publ Comp; 1976: 399-411.
    Google Scholar
  • 26 Miller MCI, Westphal MC, Reigart JR, Barner C. Medical Diagnostic Models: A Bibliography. Ann Arbor, Mich: University Microfilms International; 1977
    Google Scholar
  • 27 Good IJ. Good Thinking: The Foundations of Probability and Its Applications. Minneapolis, MN: Univ of Minnesota Press; 1983
    Google Scholar
  • 28 Miller RA. Personal communication. 1987
    PubMedGoogle Scholar
  • 29 Lawrence L. Detailed diagnosis and surgical procedures for patients discharged from short-stay hospitals. Vital & Helath Statistics 1986; 13.
    PubMedGoogle Scholar
  • 30 Health Care Financing Administration. International Classification of Diseases (Ninth Revision) – Clinical Modification. Washington, D. C: U.S. Department of Health and Human Services, Public Health Service; 1980
    Google Scholar
  • 31 Johnson RW. Independence and Bayesian updating methods. Artif Intell 1986; 29: 217-22.
    CrossrefPubMedGoogle Scholar
  • 32 Shwe MA, Middleton B, Heckerman DE, Henrion M, Horvitz EJ, Lehmann HP, Cooper GF. Probabilistic Diagnosis Using a Reformulation of the INTERNIST-1/QMR Knowledge Base – Part 1: The Probabilistic Model and Inference Algorithms. Knowledge Systems Laboratory Memo no. KSL-90-09. Stanford, CA: Stanford University; 1990
    Google Scholar
  • 33 Dongarra JJ. Performance of Various Computers Using Standard Linear Equations Software in a FORTRAN Environment. Mathematics and Computer Science Division Technical Memorandum no. 23. Ar-gonne IL: Argonne National Laboratory; 1988
    Google Scholar
  • 34 Cooper GF. The computational complexity of probabilistic inference using Bayesian belief networks. Artif Intell 1990; 42: 393-405.
    CrossrefPubMedGoogle Scholar
  • 35 Henrion M. An introduction to algorithms for inference in belief nets. In: Machine Intelligence and Pattern Recognition: Uncertainty in Artificial Intelligence 5. Henrion M, Shachter R, Kanal LN, Lemmer JF. eds. Amsterdam: North-Holland Publ Comp; 1990: 129-38.
    Google Scholar
  • 36 Fung R, Chang KC. Weighting and integrating evidence for stochastic simulation in Bayesian networks. In: Machine Intelligence and Pattern Recognition: Uncertainty in Artificial Intelligence 5. Henrion M, Shachter R, Kanal LN, Lemmer JF. eds. Amsterdam: North-Holland Publ Comp; 1990: 209-20.
    Google Scholar
  • 37 Shachter RD, Peot M. Simulation approaches to general probabilistic inference on belief networks. In: Machine Intelligence and Pattern Recognition: Uncertainty in Artificial Intelligence 5. Henrion M, Shachter R, Kanal LN, Lemmer JF. eds. Amsterdam: North-Holland Publ Comp; 1990: 221-31.
    Google Scholar
  • 38 Rubinstein RY. Simulation and the Monte Carlo Method. New York: John Wiley & Sons; 1981
    Google Scholar
  • 39 Geweke J. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 1989; 57: 1317-39.
    CrossrefPubMedGoogle Scholar
  • 40 Heckerman DE, Miller RA. Towards a better understanding of the INTERNIST-1 knowledge base. In: MEDINFO 86. Salamon R, Blum B, Jørgensen M. eds. Amsterdam: North-Holland Publ Comp; 1986: 22-6.
    Google Scholar
  • 41 Olesen KG, Kjaerulff U, Jensen E, Jensen FV, Falck B, Andreassen S, Andersen SK. A MUNIN networkfor the median nerve - a case study on loops. Appl Artif Intell 1989; 03: 384-404.
    PubMedGoogle Scholar
  • 42 Lauritzen SL, Spiegelhalter DJ. Local computations with probabilities on graphical structures and their applications to expert systems (with discussion). J Royal Statist Soc 1988; 50: 157-224.
    PubMedGoogle Scholar