Selection of clinical trials: Knowledge representation and acquisition

Savvas Nikiforou

Masters Thesis, Computer Science and Engineering Department, University of South Florida, 2002.


When medical researchers test a new treatment procedure, they recruit patients with appropriate health problems and 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 matching of medical records with a list of eligibility criteria.

A recent project at the University of South Florida has been aimed at the automation of this task. The project has involved the development of an expert system that selects matching clinical trials for each patient. If a patient's data are not sufficient for choosing a trial, the system suggests additional medical tests.

We report the work on the representation and entry of the related selection criteria and medical tests.  We first explain the structure of the system's knowledge base, which describes clinical trials and criteria for selecting patients.  We then present an interface that enables a clinician to add new trials and selection criteria without the help of a programmer. Experiments show that the addition of a new clinical trial takes ten to twenty minutes, and that novice users learn the full functionality of the interface in about an hour.