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
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Banks G (1986) Artificial intelligence in medical diagnosis: the Internist/Caduceus approach. Crit Rev Med Inform 1(1):23–54PubMedGoogle Scholar
Barnett GO, Cimino JJ et al (1987) DXplain. An evolving diagnostic decision-support system. JAMA 258(1):67–74, 3PubMedCrossRefGoogle Scholar
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
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
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
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
Shortliffe EH (1986) Medical expert systems–knowledge tools for physicians. West J Med 6:830–839Google Scholar
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
Singh H, Sethi S et al (2007) Errors in cancer diagnosis: current understanding and future directions. J Clin Oncol 25(31):5009–5018PubMedCrossRefGoogle Scholar