Clinical trials are key and essential processes for researchers to develop new treatments as well as evaluate their effectiveness and safety, whilst more than half of all clinical trials experience delays, which leads to a considerable amount of cost. In this paper, we present a cost-effective framework to reduce the time and monetary cost in the stage of recruiting and screening eligible clinical trial participants. By leveraging patients' observed conditions and the cost of medical examinations, the proposed framework uses collaborative filtering techniques to predict the utilized cost for the to-do medical examinations and then rank patients and medical examinations. The preliminary experiment results indicate that the framework is promising to reduce the cost spent on medical examinations by three quarters or even more and accelerate the recruitment process in the screening stage.
Keywords: Cost Savings; Eligibility Determination; Machine Learning.