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.

Abstract – When clinicians test a new treatment procedure, they need to identify and recruit patients with appropriate medical conditions. We have developed an expert system that helps clinicians select patients for experimental treatments, and to reduce the number and overall cost of related medical tests. We describe experiments on selecting patients for new treatments at the Moffitt Cancer Center. The experiments have shown that the system can increase the number of selected patients by a factor of three, and that it can also reduce the cost of the selection process.

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A clinical trial is a study conducted on a group of patients to evaluate a new treatment procedure. Usually, clinicians manually select patients for a clinical trial; the choice of eligible patients is a labor-intensive process, and clinicians are often unable to identify sufficient number of patients, which delays the evaluation of new treatments. We have developed a webbased system that helps clinicians to determine the eligibility of patients for multiple clinical trials. It uses probabilistic techniques that minimize the amount of manual data entry, by ordering the related data-entry steps. We describe the developed system and give the results of applying it to retrospective data of breast cancer patients at the Moffitt Cancer Center. 1.

This paper reports on the issues involved in improving accruals for cancer-related clinical trials. Clinical trials aim to determine the efficacy of novel therapies and must be conducted before new medical treatments become available to the public. In order for the clinical trial to be successful, a predetermined number of patients with an appropriate set of medical conditions must be accrued. Many of the cancer-related clinical trials in the US fail to accrue the required number of participants in the projected time frame or sometimes at all. An automated intelligent system is proposed to help physicians screen patients and increase accrual for clinical trials. Methods: We have implemented a web-based expert system at the H. Lee Moffitt Cancer Center & Research Institute in the Gastrointestinal Tumor Clinic to help physicians screen patients for phase II clinical trials. The participatory design model was used to make expert system userfriendly; optimization techniques such as data mining and statistical analysis were used to decrease the amount of data entry. Our system allows physicians to screen a patient for multiple trials simultaneously. Data available from clinical deployment of the expert system is shown and analyzed.