Using Belief Functions for Uncertainty Management and Knowledge Acquisition: An Expert Application
This work addresses uncertainty management and knowledge acquisition in medical diagnosis, but it appears incremental as it compares existing methods in a specific application.
The paper tackled the problem of medical diagnosis where experts struggle to pinpoint a single disease outcome by using Belief Functions (Dempster-Shafer theory) to extract uncertainty from data and modify it with expert opinions, showing that one formulation statistically outperforms others, including Shafer's method.
This paper describes recent work on an ongoing project in medical diagnosis at the University of Guelph. A domain on which experts are not very good at pinpointing a single disease outcome is explored. On-line medical data is available over a relatively short period of time. Belief Functions (Dempster-Shafer theory) are first extracted from data and then modified with expert opinions. Several methods for doing this are compared and results show that one formulation statistically outperforms the others, including a method suggested by Shafer. Expert opinions and statistically derived information about dependencies among symptoms are also compared. The benefits of using uncertainty management techniques as methods for knowledge acquisition from data are discussed.