  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 (EHR) data and eligibility criteria of
clinical trials. However, they either depend on trial-specific expert rules
that cannot expand to other trials or perform matching at a very general level
with a black-box model where the lack of interpretability makes the model
results difficult to be adopted.
  To provide accurate and interpretable patient trial matching, we introduce a
personalized dynamic tree-based memory network model named TREEMENT. It
utilizes hierarchical clinical ontologies to expand the personalized patient
representation learned from sequential EHR data, and then uses an attentional
beam-search query learned from eligibility criteria embedding to offer a
granular level of alignment for improved performance and interpretability. We
evaluated TREEMENT against existing models on real-world datasets and
demonstrated that TREEMENT outperforms the best baseline by 7% in terms of
error reduction in criteria-level matching and achieves state-of-the-art
results in its trial-level matching ability. Furthermore, we also show TREEMENT
can offer good interpretability to make the model results easier for adoption.
