Mathematical Foundations of Decision Support Systems

  • S. Andrew Spooner
Part of the Health Informatics book series (HI)


Many computer applications may be considered to be clinical decision support systems. Programs that perform MEDLINE searches or check drug interactions do support decisions, but they are not “clinical decision support systems” in the usual sense. What we usually mean by a clinical decision support system is a program that supports a reasoning task, carried out behind the scenes and based on clinical data. For example, a program that accepts thyroid panel results and generates a list of possible diagnoses is what we usually recognize as a clinical diagnostic decision support system (CDDSS). General purpose programs that accept clinical findings and generate diagnoses are the typical CDDSS. These programs employ numerical and logical techniques to convert clinical input into the kind of information that a physician might use in performing a diagnostic reasoning task. How these numerical techniques work is the subject of this chapter.


Bayesian Network Decision Support System Kawasaki Disease Mathematical Foundation Inference Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Behrman RE, Kliegman RM, Arvin AM, eds. Nelson Textbook of Pediatrics, 15th ed. Philadelphia: W.B. Saunders Company, 1996.Google Scholar
  2. 2.
    Shiomi S, Kuroki T, Jomura H et al. Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J Nuclear Med 1995; 36:593–598.Google Scholar
  3. 3.
    Suryanarayanan S, Reddy NP, Canilang EP. A fuzzy logic diagnosis system for classification of pharyngeal dysphagia. Int J Biomed Comput 1995; 38:207–215.PubMedCrossRefGoogle Scholar
  4. 4.
    Shortliffe EH. Computer-Based Medical Consultations: MYCIN. New York, NY: Elsevier Computer Science Library, Artificial Intelligence Series, 1976.Google Scholar
  5. 5.
    Kahn MG, Steib SA, Fraser VJ et al. An expert system for culture-based infection control surveillance. Proc Annu Symp Comput Appi Med Care 1993:171–175.Google Scholar
  6. 6.
    Emori TG, Culver DH, Horan TC et al. National Nosocomial Infections Surveillance System (NNIS): description of surveillance methodology. Am J Infect Control 1991; 19:19–35.PubMedCrossRefGoogle Scholar
  7. 7.
    Bayes T. An essay towards solving a problem in the doctrine of chances. Philosophical Transactions 1763; 3:370–418.CrossRefGoogle Scholar
  8. 8.
    Warner HR, Toronto AF, Veasey LG et al. Mathematical approach to medical diagnosis. JAMA 1961; 177:75–81.CrossRefGoogle Scholar
  9. 9.
    Warner HR Jr. Iliad: moving medical decision-making into new frontiers. Methods Inf Med 1989; 28:370–372.PubMedGoogle Scholar
  10. 10.
    Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975; 23:351–379.CrossRefGoogle Scholar
  11. 11.
    Miller R, Masarie FE, Myers J. Quick Medical Reference (QMR) for diagnostic assistance. MD Comput 1986; 3:34–48.PubMedGoogle Scholar
  12. 12.
    Barnett GO, Cimino JJ, Hupp JA et al. DXplain—an evolving diagnostic decision-support system. JAMA 1987; 258:67–74.PubMedCrossRefGoogle Scholar
  13. 13.
    Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science 1974; 188:1124–1131.CrossRefGoogle Scholar
  14. 14.
    Dawes RM, Faust D, Meehl PE. Clinical versus actuarial judgment. Science 1989; 243:1668–1674.PubMedCrossRefGoogle Scholar
  15. 15.
    Gigerenzer G, Hoffrage U. How to improve Bayesian reasoning without instruction: frequency formats. Psychol Rev 1995; 102:684–704.CrossRefGoogle Scholar
  16. 16.
    Forsythe DE, Buchanan BG, Osheroff JA et al. Expanding the concept of medical information: an observational study of physicians’ information needs. Comput Biomed Res 1992; 25:181–200.PubMedCrossRefGoogle Scholar
  17. 17.
    Aliferis CF, Cooper GF, Miller RA et al. A temporal analysis of QMR. JAMIA 1996; 3:79–91.PubMedCrossRefGoogle Scholar
  18. 18.
    Kahn CE, Roberts LM, Wang K et al. Preliminary investigation of a Bayesian network for mammographic diagnosis of breast cancer. Proc Annu Symp Comput Appl Med Care 1995:208–212.Google Scholar
  19. 19.
    Miller PL, Frawley SJ, Sayward FG et al. IMM/Serve: An Internet-Accessible Rule-Based Program for Childhood Immunization. Proc Annu Symp Comput Appl Med Care 1995:208–212.Google Scholar
  20. 20.
    Porter JF, Kingsland LC d, Lindberg DA et al. The AI/RHEUM knowledge-based computer consultant system in rheumatology. Performance in the diagnosis of 59 connective tissue disease patients from Japan. Arthritis Rheum 1988; 31:219–226.PubMedCrossRefGoogle Scholar
  21. 21.
    Tu SW, Eriksson H, Gennari JH et al. Ontology-based configuration of problem-solving methods and generation of knowledge-acquisition tools: application of PROTEGE-II to protocol-based decision support. Artif Intell Med 1995; 7:257–289.PubMedCrossRefGoogle Scholar
  22. 22.
    Bar-Hillel M. The base-rate fallacy in probability judgments. Acta Psychol 1980; 44:211–233.CrossRefGoogle Scholar
  23. 23.
    Musen MA, van der Lei J. Knowledge engineering for clinical consultation programs: modeling the application area. Methods Inf Med 1989; 28:28–35.PubMedGoogle Scholar
  24. 24.
    Mann NH 3d, Brown MD. Artificial intelligence in the diagnosis of low back pain. Orthop Clin North Am 1991; 22:303–314.PubMedGoogle Scholar
  25. 25.
    Wu Y, Giger ML, Doi K et al. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 1993; 187:81–87.PubMedGoogle Scholar
  26. 26.
    Astion ML, Wener MH, Thomas RG et al. Application of neural networks to the classification of giant cell arteritis. Arthritis Rheum 1994; 37:760–770.PubMedCrossRefGoogle Scholar
  27. 27.
    Baxt WG. Use of an artificial neural network for the diagnosis of acute myocardial infarction. Ann Intern Med 1991; 115:843–848.PubMedCrossRefGoogle Scholar
  28. 28.
    Levin M. Use of genetic algorithms to solve biomedical problems. MD Comput 1995; 12:193–199.PubMedGoogle Scholar
  29. 29.
    Grzymala-Busse JW, Woolery LK. Improving prediction of preterm birth using a new classification scheme and rule induction. Proc Annu Symp Comput Appl Med Care 1994:730–734.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • S. Andrew Spooner

There are no affiliations available

Personalised recommendations