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Clinical trials are critical for drug development but often suffer fromexpensive and inefficient patient recruitment. In recent years, machinelearning models have been proposed for speeding up patient recruitment viaautomatically matching patients with clinical trials based on longitudinalpatient electronic health records (EHR) data and eligibility criteria ofclinical trials. However, they either depend on trial-specific expert rulesthat cannot expand to other trials or perform matching at a very general levelwith a black-box model where the lack of interpretability makes the modelresults difficult to be adopted.  To provide accurate and interpretable patient trial matching, we introduce apersonalized dynamic tree-based memory network model named TREEMENT. Itutilizes hierarchical clinical ontologies to expand the personalized patientrepresentation learned from sequential EHR data, and then uses an attentionalbeam-search query learned from eligibility criteria embedding to offer agranular level of alignment for improved performance and interpretability. Weevaluated TREEMENT against existing models on real-world datasets anddemonstrated that TREEMENT outperforms the best baseline by 7% in terms oferror reduction in criteria-level matching and achieves state-of-the-artresults in its trial-level matching ability. Furthermore, we also show TREEMENTcan offer good interpretability to make the model results easier for adoption.

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