Performance evaluation of medical expert systems using ROC curves.
This paper presents a performance evaluation of the diagnostic accuracy of the medical expert system CADIAG-2/PANCREAS. The study included 47 clinical cases from a university hospital with 51 diagnosis of pancreatic diseases (four patients had two pancreatic diseases). As gold standard, the histologically or clinically confirmed diagnoses were assumed. Performance was studied along three lines: (a) each case was evaluated twice, first, by restricting patient data to history, physical examination, and basic laboratory tests and, second, by utilizing the complete set of data including also special laboratory tests. US. X ray, CT-scan, ECG, and biopsy, if available: (b) considering CADIAG-2's hypotheses generation, each evaluation series was also carried out twice, first, by testing whether the gold standard was the first diagnosis in the ranked list of hypothesis and, second, whether the gold standard was among the hypotheses: (c) receiver operating characteristic (ROC) curves were determined by varying an internal threshold which determined the extent of CADIAG-2's diagnostic hypotheses generation. The evaluation showed that CADIAG-2's initial list of diagnostic hypotheses, based on patient history, physical examination, and basic laboratory tests usually has already included the gold standard diagnosis and thus an application of CADIAG-2 at a very early stage of the diagnostic process seems achievable. Moreover, it turned out that given the complete set of patient's medical data the gold standard is usually ranked at the first place in the list of hypotheses. except for patients with chronic diseases where only unspecific findings are available. The last test series showed that ROC curves do not only allow optimal adjustment of the expert system's internal ad hoc decision criteria such as thresholds, weights, and scores but also provide a basis for better comparing the performance of different medical expert systems.
Adlassnig
K P
KP
Department of Medical Computer Sciences, University of Vienna, Austria.
Scheithauer
W
W
D003936:Diagnosis, Computer-Assisted; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D006801:Humans; D007388:Internal Medicine; D010182:Pancreatic Diseases / Q000175:diagnosis; D012372:ROC Curve