TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-based Memory Network

Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials based on longitudinal patient electronic health records (EHRs) and eligibility criteria of trials. However, they either depend on trial-specific expert rules that cannot be generalized or perform matching more generally with a black-box model where the lack of interpretability makes the model results difficult to be adopted.