Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis
This work addresses diagnostic accuracy for veterinarians, but it appears incremental as it builds on existing methods without claiming major breakthroughs.
The paper tackled improving veterinary medical diagnosis by applying induction methods to extract knowledge from hospital data, enhancing diagnostic charts and statistical methods to find more significant variables, and presented results comparing a Bayesian-fuzzy evidence combination method with other techniques.
This paper discusses a project undertaken between the Departments of Computing Science, Statistics, and the College of Veterinary Medicine to design a medical diagnostic system. On-line medical data has been collected in the hospital database system for several years. A number of induction methods are being used to extract knowledge from the data in an attempt to improve upon simple diagnostic charts used by the clinicians. They also enhance the results of classical statistical methods - finding many more significant variables. The second part of the paper describes an essentially Bayesian method of evidence combination using fuzzy events at an initial step. Results are presented and comparisons are made with other methods.