Computer diagnosis in XXXX . Bayes' rule founded on 1002 consecutive cases.

@entity1 @entity1087
Extensive clinical and clinical chemical information was collected from 1002 consecutive @entity1087 @entity1 . Initial selection of variables based on Chi 2-tests or Mann-Whitney U-test allowed the removal of 64 of the 107 variables originally collected. A further selection of variables was carried out using a modified version of Bayes' rule thus reducing the number of variables from 43 to 22. Of the 982 @entity1 with a final diagnosis 743 @entity1 (76%) could be classified correctly into one of 13 diagnostic categories. The Bayes' rule was also applied to a test group of a further 110 @entity1087 @entity1 and found to perform equally well: of 108 @entity1 with a final diagnosis 81 (75%) were correctly classified. A comparison between the clinician's diagnosis and the computer-aided diagnosis according to Bayes' rule demonstrated agreement with regard to one of the 13 diagnostic alternatives in 734 @entity1 (75%), of whom 81 @entity1 were wrongly diagnosed. In the test group agreement upon diagnosis was found in 80 @entity1 (74%). By plausibly combining the computer-aided and the clinician's preliminary diagnoses, more correct classifications were obtained than with either method alone. Many diagnostic modalities such as ultrasound examination, CT-scan, and direct cholangiography are at hand today for the differential diagnosis of @entity1087 . Computer-aided diagnosis using Bayes' rule has proved a reliable tool for the clinician and can be used in the planning of a diagnostic strategy for the individual @entity1087 @entity1 .
