  Significant advancements have been made in recent years to optimize patient
recruitment for clinical trials, however, improved methods for patient
recruitment prediction are needed to support trial site selection and to
estimate appropriate enrollment timelines in the trial design stage. In this
paper, using data from thousands of historical clinical trials, we explore
machine learning methods to predict the number of patients enrolled per month
at a clinical trial site over the course of a trial's enrollment duration. We
show that these methods can reduce the error that is observed with current
industry standards and propose opportunities for further improvement.
