  Clinical trials are essential for drug development but often suffer from
expensive, inaccurate and insufficient patient recruitment. The core problem of
patient-trial matching is to find qualified patients for a trial, where patient
information is stored in electronic health records (EHR) while trial
eligibility criteria (EC) are described in text documents available on the web.
How to represent longitudinal patient EHR? How to extract complex logical rules
from EC? Most existing works rely on manual rule-based extraction, which is
time consuming and inflexible for complex inference. To address these
challenges, we proposed DeepEnroll, a cross-modal inference learning model to
jointly encode enrollment criteria (text) and patients records (tabular data)
into a shared latent space for matching inference. DeepEnroll applies a
pre-trained Bidirectional Encoder Representations from Transformers(BERT) model
to encode clinical trial information into sentence embedding. And uses a
hierarchical embedding model to represent patient longitudinal EHR. In
addition, DeepEnroll is augmented by a numerical information embedding and
entailment module to reason over numerical information in both EC and EHR.
These encoders are trained jointly to optimize patient-trial matching score. We
evaluated DeepEnroll on the trial-patient matching task with demonstrated on
real world datasets. DeepEnroll outperformed the best baseline by up to 12.4%
in average F1.
