Medical data base system with an ability of automated diagnosis.
We carried out an experiment on a medical information system in which a clinical data base is combined organically with computer programs for automated diagnosis. In this system, the parameters for automated diagnosis are devised to be renewed as the contents of the data base (patient's information) increase. This system can be regarded as a data base possessing a kind of diagnosing ability which grows up with time. We have named this system "Intelligent Data Base". The algorithm for computer diagnosis used in this study is based on maximum likelihood method, and each likelihood is weighted with a prior probability of each disease. The discrimination efficiency of this method is logically equal to that of the Bayes rule. First 27 cases were learnt by the system and correct diagnosis was obtained in 78% of the cases. When cases for learning increased to 82, the percentage of correct diagnosis was improved to 95%.
D001933:Brain Stem; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D004660:Encephalitis / Q000175:diagnosis; D006801:Humans; D009103:Multiple Sclerosis / Q000175:diagnosis*; D009187:Myelitis / Q000175:diagnosis; D011843:Radiculopathy / Q000175:diagnosis