Syndrome Diagnosis: Human Intuition or Machine Intelligence?
Øivind Braaten*, Johannes Friestad
Identifiers and Pagination:Year: 2008
First Page: 149
Last Page: 159
Publisher Id: TOMINFOJ-2-149
Article History:Received Date: 21/8/2008
Revision Received Date: 21/9/2008
Acceptance Date: 21/10/2008
Electronic publication date: 19/11/2008
Collection year: 2008
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
The aim of this study was to investigate whether artificial intelligence methods can represent objective methods that are essential in syndrome diagnosis. Most syndromes have no external criterion standard of diagnosis. The predictive value of a clinical sign used in diagnosis is dependent on the prior probability of the syndrome diagnosis. Clinicians often misjudge the probabilities involved. Syndromology needs objective methods to ensure diagnostic consistency, and take prior probabilities into account. We applied two basic artificial intelligence methods to a database of machine-generated patients - a ‘vector method’ and a set method. As reference methods we ran an ID3 algorithm, a cluster analysis and a naive Bayes’ calculation on the same patient series. The overall diagnostic error rate for the the vector algorithm was 0.93%, and for the ID3 0.97%. For the clinical signs found by the set method, the predictive values varied between 0.71 and 1.0. The artificial intelligence methods that we used, proved simple, robust and powerful, and represent objective diagnostic methods.