What is an Optimal Diagnosis?
  Within diagnostic reasoning there have been a number of proposed definitions
of a diagnosis, and thus of the most likely diagnosis, including most probable
posterior hypothesis, most probable interpretation, most probable covering
hypothesis, etc. Most of these approaches assume that the most likely diagnosis
must be computed, and that a definition of what should be computed can be made
a priori, independent of what the diagnosis is used for. We argue that the
diagnostic problem, as currently posed, is incomplete: it does not consider how
the diagnosis is to be used, or the utility associated with the treatment of
the abnormalities. In this paper we analyze several well-known definitions of
diagnosis, showing that the different definitions of the most likely diagnosis
have different qualitative meanings, even given the same input data. We argue
that the most appropriate definition of (optimal) diagnosis needs to take into
account the utility of outcomes and what the diagnosis is used for.
