Evaluating the quality of a probabilistic diagnostic system using different inferencing strategies.
In this paper we describe the evaluation of a probabilistic diagnostic system for patients with renal mass. Three inference models: Multi-membership Bayesian (MB), Minimal Diagnosis (MD) and Bayesian Network (BN), and 72 patients are used to illustrate three interrelated measures of system performance: accuracy, reliability and discriminating power. The inferencing strategies we tested demonstrated the kind of trade-offs in the performance measures that can be expected from imperfect systems. Ultimately, the purpose and expected use of a system should dictate the relative importance ascribed to different aspects of system performance.
Li
Y C
YC
Department of Medical Informatics, University of Utah, Salt Lake City.
Haug
P J
PJ
D000293:Adolescent; D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D002648:Child; D003936:Diagnosis, Computer-Assisted; D005069:Evaluation Studies as Topic; D005103:Expert Systems; D005260:Female; D006801:Humans; D007256:Information Systems; D007674:Kidney Diseases / Q000175:diagnosis*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011336:Probability; D014584:User-Computer Interface