Medical Diagnostic Decision Support

Part of the Health Informatics book series (HI)


Medical diagnostic decision-making is a complex task that consists in finding the right diagnosis from the signs and symptoms presented by a patient. Computers have rapidly been considered as potential diagnostic aids in medical decision-making. This chapter first presents medical diagnostic modeling as a hypothetico-deductive reasoning process. Then, the different approaches developed to provide computerized medical diagnostic decision support are proposed. Initial numerical approaches, either statistical or probabilistic, are first presented. Examples of clinical scores, more recently developed, are given. Then, medical expert systems are described. The three components of an expert system, the knowledge base, the base of facts, and the inference engine, are introduced. A focus is given on knowledge representation formalisms with the description of production rules, decision trees, semantic networks, and frames. The subsection describing the inference engine starts with a presentation of the three types of inference (deduction, induction, abduction). The principles of formal logic are given and the main ways the inference engine may operate are described (forward and backward chainings). Finally, historical medical expert systems, such as Mycin and Internist, as well as systems currently available for medical diagnostic decision support (DXplain™) are described.


Decision support Expert systems Knowledge base Production rules Decision trees Inference engine Forward chaining Backward chaining 


  1. Banks G (1986) Artificial intelligence in medical diagnosis: the Internist/Caduceus approach. Crit Rev Med Inform 1(1):23–54PubMedGoogle Scholar
  2. Barnett GO, Cimino JJ et al (1987) DXplain. An evolving diagnostic decision-support system. JAMA 258(1):67–74, 3PubMedCrossRefGoogle Scholar
  3. De Dombal FT, Leaper DJ et al (1972) Computer-aided diagnosis of acute abdominal pain. Br Med J 2(5804):9–13PubMedCrossRefGoogle Scholar
  4. Elkin PL, Liebow M et al (2010) The introduction of a diagnostic decision support system (DXplain™) into the workflow of a teaching hospital service can decrease the cost of service for diagnostically challenging Diagnostic Related Groups (DRGs). Int J Med Inform 79(11):772–777PubMedCrossRefGoogle Scholar
  5. Elstein AS, Schwarz A (2002) Clinical problem solving and diagnostic decision-making: selective review of the cognitive literature. Br Med J 324(7339):729–732CrossRefGoogle Scholar
  6. Gandhi TK, Kachalia A et al (2006) Missed and delayed diagnoses in the ambulatory setting: a study of closed malpractice claims. Ann Intern Med 145(7):488–496PubMedCrossRefGoogle Scholar
  7. Horrocks JC, McCann AP et al (1972) Computer-aided diagnosis: description of year adaptable system, and operational experience with 2.034 cases. Br Med J 2(5804):5–9PubMedCrossRefGoogle Scholar
  8. Ledley RS, Lusted LB (1959) Reasoning foundations of medical diagnosis. Science 130:9–21PubMedCrossRefGoogle Scholar
  9. Lindsay RK, Buchanan BG et al (1993) DENDRAL: a case study of the first expert system for scientific hypothesis formation. Artif Intell 61(2):209–261CrossRefGoogle Scholar
  10. Lucas AE, Smeenk FJ et al (2012) Diagnostic accuracy of primary care asthma/COPD working hypotheses, a real life study. Respir Med 106(8):1158–1163PubMedCrossRefGoogle Scholar
  11. Miller RA (1994) Medical diagnostic decision support systems- past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc 1:8–27PubMedCrossRefGoogle Scholar
  12. Miller RA (2009) Computer-assisted diagnostic decision support: history, challenges, and possible paths forward. Adv Health Sci Educ 14:89–106CrossRefGoogle Scholar
  13. Miller RA, Masarie FE (1989) Use of the Quick Medical Reference (QMR) program as a tool for medical education. Methods Inf Med 28(4):340–345PubMedGoogle Scholar
  14. Miller RA, Pople HE Jr et al (1982) Internist-1, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 307(8):468–476PubMedCrossRefGoogle Scholar
  15. Minsky M (1975) A framework for representing knowledge. In: Winston P (ed) The psychology of computer vision. Mc Graw Hill, New York, pp 211–277Google Scholar
  16. Nakache JP (1976) Multidimensional data analysis in medical decision. In: De Dombal FT, Grémy F (eds) Decision making and medical care. Amsterdam, North HollandGoogle Scholar
  17. Schiff GD, Hasan O et al (2009) Diagnostic error in medicine: analysis of 583 physician-reported errors. Arch Intern Med 169(20):1881–1887PubMedCrossRefGoogle Scholar
  18. Schoolman HM, Bernstein LM (1978) Computer use in diagnosis, prognosis, and therapy. Science 200(4344):926–931PubMedCrossRefGoogle Scholar
  19. Shortliffe EH (1986) Medical expert systems–knowledge tools for physicians. West J Med 6:830–839Google Scholar
  20. Shortliffe EH, Davis R et al (1975) Computer-based consultations in clinical therapeutics: explanation and rule acquisition capabilities of the Mycin system. Comput Biomed Res 4:303–320CrossRefGoogle Scholar
  21. Singh H, Sethi S et al (2007) Errors in cancer diagnosis: current understanding and future directions. J Clin Oncol 25(31):5009–5018PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag France 2014

Authors and Affiliations

  1. 1.Département de Santé Publique, UFR de MédecineUPMC, Paris 6, Hôpital TenonParisFrance
  2. 2.Laboratoire d’Informatique Médicale, Faculté de MédecineINSERM U936RennesFrance
  3. 3.LIM&BIO EA 3969, UFR SMBHUniversité Paris 13Bobigny CedexFrance

Personalised recommendations