Knowledge Acquisition for Clinical-Trial Selection

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.