Using Machine Learning to Recommend Oncology Clinical Trials

Abstract

Clinical trials serve an important role in oncology, not only advancing medical science but also offering patients promising therapy before it is widely available. Memorial Sloan Kettering Cancer Center (MSK) conducts over 500 therapeutic trials at one time; most are focused on a single type of cancer (e.g. breast, lung) reflecting subspecialized nature of care. However, clinical trial accrual is a challenge as patient-trial matching is a slow and manual process. We address this challenge via a machine learning-powered clinical trial recommendation engine designed to be deployed at the point of care.