Revision of diagnostic logic using a clinical database

Abstract

Statistical pattern-recognition techniques have been frequently applied to the problem of medical diagnosis. Sequential Bayesian approaches are appealing because of the possibility of generating the underlying sensitivities, specificities, and prevalence statistics from the estimates of medical experts. The accuracy of these estimates and the consequences of inaccuracies carry implications for the future development of this type of system. In an effort to explore these subjects, the authors used statistics derived from a clinical database to revise the diagnostic logic in a Bayesian system for generating a differential diagnostic list. Substantial changes in estimated a priori probabilities, sensitivities, and specificities were made to correct for significant under- and overestimations of these values by a group of medical experts. The system based on the derived values appears to perform better than the original system. It is concluded that the statistics used in a Bayesian diagnostic system should be derived from a database representative of the patient population for which the system is designed.