  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.
