Automated discovery of positive and negative knowledge in clinical databases.
In this article, the characteristics of two measures, classification accuracy and coverage, were discussed. We showed that both measures are dual, and that accuracy and coverage are measures of both positive and negative rules, respectively. Then, an algorithm for induction of positive and negative rules was introduced. The proposed method was evaluated on medical databases, and the experimental results show that induced rules correctly represented expert knowledge. Several interesting patterns were also discovered.
Department of Medical Informatics, Shimane Medical University, School of Medicine. tsumoto@computer.org
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D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D002561:Cerebrovascular Disorders / Q000175:diagnosis; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D006261:Headache / Q000175:diagnosis; D006801:Humans; D008581:Meningitis / Q000175:diagnosis; D015233:Models, Statistical; D008962:Models, Theoretical