A Decision-Theoretic Model for Using Scientific Data
  Many Artificial Intelligence systems depend on the agent's updating its
beliefs about the world on the basis of experience. Experiments constitute one
type of experience, so scientific methodology offers a natural environment for
examining the issues attendant to using this class of evidence. This paper
presents a framework which structures the process of using scientific data from
research reports for the purpose of making decisions, using decision analysis
as the basis for the structure and using medical research as the general
scientific domain. The structure extends the basic influence diagram for
updating belief in an object domain parameter of interest by expanding the
parameter into four parts: those of the patient, the population, the study
sample, and the effective study sample. The structure uses biases to perform
the transformation of one parameter into another, so that, for instance,
selection biases, in concert with the population parameter, yield the study
sample parameter. The influence diagram structure provides decision theoretic
justification for practices of good clinical research such as randomized
assignment and blindfolding of care providers. The model covers most research
designs used in medicine: case-control studies, cohort studies, and controlled
clinical trials, and provides an architecture to separate clearly between
statistical knowledge and domain knowledge. The proposed general model can be
the basis for clinical epidemiological advisory systems, when coupled with
heuristic pruning of irrelevant biases; of statistical workstations, when the
computational machinery for calculation of posterior distributions is added;
and of meta-analytic reviews, when multiple studies may impact on a single
population parameter.
