2 research outputs found
Induction and Uncertainty Management Techniques Applied to Veterinary Medical Diagnosis
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.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in
Artificial Intelligence (UAI1988
Experiments Using Belief Functions and Weights of Evidence incorporating Statistical Data and Expert Opinions
This paper presents some ideas and results of using uncertainty management
methods in the presence of data in preference to other statistical and machine
learning methods. A medical domain is used as a test-bed with data available
from a large hospital database system which collects symptom and outcome
information about patients. Data is often missing, of many variable types and
sample sizes for particular outcomes is not large. Uncertainty management
methods are useful for such domains and have the added advantage of allowing
for expert modification of belief values originally obtained from data.
Methodological considerations for using belief functions on statistical data
are dealt with in some detail. Expert opinions are Incorporated at various
levels of the project development and results are reported on an application to
liver disease diagnosis. Recent results contrasting the use of weights of
evidence and logistic regression on another medical domain are also presented.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989