Pediatric Informatics: Computer Applications in Child Health (Health ...

Chapter 12
Diagnostic Decision Support
Mitchell J. Feldman
Objectives
To describe the history and previous work in the field of medical diagnostic
decision support, including Bayes’ Law, on which much of this work is based
To illustrate three currently available systems, DXplain, GIDEON, and Isabel
To consider how diagnostic decision support could be useful in practice
12.1 Introduction
In 1763, the Reverend Thomas Bayes published “a method by which we might
judge concerning the probability that an event has to happen, in given circumstances,
upon supposition that we know nothing concerning it but that, under the
same circumstances, it has happened a certain number of times, and failed a certain
other number of times.” 1 This is Bayes’ Law, which in 1959, was applied to medical
diagnosis 2 to form the basis of diagnostic decision support. 3, 4 Because almost all
computer-based medical diagnostic decision support systems (MDDSS) use some
application of Bayes’ Law, it is instructive to review basic principles.
12.2 Testing: Sensitivity and Specificity
Medical decision making is predicated on an explicit or implicit use of sensitivity,
specificity, and prevalence:
The sensitivity of a test is the probability that a test for a disease will be positive
in a patient with the disease. A highly sensitive test will be positive in most cases
in patients with the disease. In a test of low sensitivity, many patients with the
disease will test negative.
The specificity of a test is the probability that the test will be negative in a
patient without the disease. A highly specific test will be negative in most cases
C.U. Lehmann et al. (eds.), Pediatric Informatics: Computer Applications 161
in Child Health, Health Informatics,
© Springer Science + Business Media, LLC 2009