Case-based explanation for medical diagnostic programs, with an example from gynaecology.
One of the most accountable methods of providing machine assistance in medical diagnosis is to retrieve and display similar previously diagnosed cases from a database. In practice, however, classifying cases according to the diagnoses of their nearest neighbours is often significantly less accurate than other statistical classifiers. In this paper the transparency of the nearest neighbours method is combined with the accuracy of another statistical method. This is achieved by using the other statistical method to define a measure of similarity between the presentations of two cases. The diagnosis of abdominal pain of suspected gynaecological origin is used as a case study to evaluate this method. Bayes' theorem, with the usual assumption of conditional independence, is used to define a metric on cases. This new metric was found to correspond as well as Hamming distance to the clinical notion of "similarity" between cases, while significantly increasing accuracy to that of the Bayes' method itself.
Stamper
R
R
Programming Research Group, Oxford University Computing Laboratory, UK.
Todd
B S
BS
Macpherson
P
P
D000006:Abdomen, Acute / Q000209:etiology*; D000293:Adolescent; D000328:Adult; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005103:Expert Systems; D005260:Female; D005831:Genital Diseases, Female / Q000150:complications / Q000175:diagnosis*; D006801:Humans; D007256:Information Systems; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D011247:Pregnancy; D011271:Pregnancy, Ectopic / Q000150:complications / Q000175:diagnosis; D012984:Software