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Clinical Decision Support Systems pp 23–43Cite as

Mathematical Foundations of Decision Support Systems

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Part of the book series: Health Informatics ((HI))

Abstract

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 (CDSS) 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 diagnostic decision support system, a particular type of CDSS. General purpose programs that accept clinical findings and generate diagnoses are typical diagnostic decision support systems. 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.

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References

  1. Behrman RE, Kliegman RM, Nelson WE, eds. Nelson textbook of pediatrics 15th ed. Philadelphia: W.B. Saunders Company; 1996.

    Google Scholar 

  2. Shiomi S, Kuroki T, Jomura H, et al. Diagnosis of chronic liver disease from liver scintiscans by fuzzy reasoning. J Nucl Med 1995;36:593–598.

    PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  4. Shortliffe EH. Computer-based medical consultations: MYCIN. New York: Elsevier; 1976.

    Google Scholar 

  5. Kahn MG, Steib SA, Fraser VJ, Dunagan WC. An expert system for culture-based infection control surveillance. Proc Annu Symp Comput Appl Med Care 1993;171–175.

    Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  7. Bayes T. An essay towards solving a problem in the doctrine of chances. 1763. MD Comput 1991;8:157–171.

    PubMed  CAS  Google Scholar 

  8. Warner HR, Toronto AF, Veasey LG, Stephenson R. A mathematical approach to medical diagnosis. Application to congenital heart disease. JAMA 1961;177: 177–183.

    PubMed  CAS  Google Scholar 

  9. Warner HR Jr. Iliad: moving medical decision-making into new frontiers. Methods Inf Med 1989;28:370–372.

    PubMed  Google Scholar 

  10. Shortliffe EH, Buchanan BG. A model of inexact reasoning in medicine. Math Biosci 1975;23:351–379.

    Article  Google Scholar 

  11. Miller R, Masarie FE, Myers JD. Quick medical reference (QMR) for diagnostic assistance. MD Comput 1986;3:34–48.

    PubMed  CAS  Google Scholar 

  12. Barnett GO, Cimino JJ, Hupp JA, Hoffer EP. DXplain. An evolving diagnostic decision-support system. JAMA 1987;258:67–74.

    Article  PubMed  CAS  Google Scholar 

  13. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science 1974;188:1124–1131.

    Article  Google Scholar 

  14. Dawes RM, Faust D, Meehl PE. Clinical versus actuarial judgment. Science 1989;243:1668–1674.

    Article  PubMed  CAS  Google Scholar 

  15. Gigerenzer G, Hoffrage U. How to improve Bayesian reasoning without instruction: frequency formats. Psychol Rev 1995;102:684–704.

    Article  Google Scholar 

  16. Forsythe DE, Buchanan BG, Osheroff JA, Miller RA. Expanding the concept of medical information: an observational study of physicians’ information needs. Comput Biomed Res 1992;25:181–200.

    Article  PubMed  CAS  Google Scholar 

  17. Aliferis CF, Cooper GF, Miller RA, Buchanan BG, Bankowitz R, Giuse N. A temporal analysis of QMR. J Am Med Inform Assoc 1996;3:79–91.

    PubMed  CAS  Google Scholar 

  18. Kahn CE Jr, Roberts LM, Wang K, Jenks D, Haddawy P. 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. Miller PL, Frawley SJ, Sayward FG, Yasnoff WA, Duncan L, Fleming DW. IMM/Serve: a rule-based program for childhood immunization. Proc AMIA Annu Fall Symp 1996:184–188.

    Google Scholar 

  20. Porter JF, Kingsland LC 3rd, 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.

    Article  PubMed  CAS  Google Scholar 

  21. Tu SW, Eriksson H, Gennari JH, Shahar Y, Musen MA. 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.

    Article  PubMed  CAS  Google Scholar 

  22. Bar-Hillel M. The base-rate fallacy in probability judgments. Acta Psychol 1980;44:211–233.

    Article  Google Scholar 

  23. Musen MA, van der Lei J. Knowledge engineering for clinical consultation programs: modeling the application area. Methods Inf Med 1989;28:28–35.

    PubMed  CAS  Google Scholar 

  24. Mann NH 3d, Brown MD. Artificial intelligence in the diagnosis of low back pain. Orthop Clin North Am 1991;22:303–314.

    PubMed  Google Scholar 

  25. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 1993;187:81–87.

    PubMed  CAS  Google Scholar 

  26. Astion ML, Wener MH, Thomas RG, Hunder GG, Bloch DA. Application of neural networks to the classification of giant cell arteritis. Arthritis Rheum 1994;37:760–770.

    Article  PubMed  CAS  Google Scholar 

  27. Baxt WG. Use of an artificial neural network for the diagnosis of acute myocardial infarction. Ann Intern Med 1991;115:843–848.

    PubMed  CAS  Google Scholar 

  28. Levin M. Use of genetic algorithms to solve biomedical problems. MD Comput 1995;12:193–199.

    PubMed  CAS  Google Scholar 

  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 

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Author information

Authors and Affiliations

  1. Division of General Pediatrics, Department of Pediatrics, University of Tennessee Health Science Center College of Medicine, Memphis, TN, 38163

    S. Andrew Spooner MD, FAAP (John Dustin Buckman Professor and Associate Professor of Pediatrics and Director)

  2. Telehealth and Informatics, Le Bonheur Children’s Medical Center, Memphis, TN, 38103, USA

    S. Andrew Spooner MD, FAAP (John Dustin Buckman Professor and Associate Professor of Pediatrics and Director)

Authors
  1. S. Andrew Spooner MD, FAAP
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Editor information

Editors and Affiliations

  1. Health Informatics Program Department of Health Services Administration School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, 35294, USA

    Eta S. Berner EdD, FACMI, FHIMSS (Professor) (Professor)

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© 2007 Springer Science+Business Media, LLC

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Spooner, S.A. (2007). Mathematical Foundations of Decision Support Systems. In: Berner, E.S. (eds) Clinical Decision Support Systems. Health Informatics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-38319-4_2

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  • DOI: https://doi.org/10.1007/978-0-387-38319-4_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-33914-6

  • Online ISBN: 978-0-387-38319-4

  • eBook Packages: MedicineMedicine (R0)

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