Knowledge acquisition for clinial-trial selection
Savvas Nikiforou, Eugene Fink, Lawrence O. Hall, Dmitry B. Goldgof, and
Jeffrey P. Krischer
In Proceedings of the IEEE International Conference
on Systems, Man, and Cybernetics, pages 60-65, 2002.
Abstract
When medical researchers test a new treatment procedure, they recruit patients
with appropriate medical histories. An experiment with a new procedure
is called a clinical trial. The selection of patients for clinical trials
has traditionally been a labor-intensive task, which involves the matching
of medical records with a list of eligibility criteria, and studies have
shown that clinicians can miss up to 60% of the eligible patients. A recent
project at the University of South Florida has been aimed at the automation
of this task. We have developed an intelligent agent that selects trials
for eligible patients. We report the work on the representation and entry
of the related knowledge about clinical trials. We describe the structure
of the agent's knowledge base and the interface for adding new trials.