  Clinical trials play important roles in drug development but often suffer
from expensive, inaccurate and insufficient patient recruitment. The
availability of massive electronic health records (EHR) data and trial
eligibility criteria (EC) bring a new opportunity to data driven patient
recruitment. One key task named patient-trial matching is to find qualified
patients for clinical trials given structured EHR and unstructured EC text
(both inclusion and exclusion criteria). How to match complex EC text with
longitudinal patient EHRs? How to embed many-to-many relationships between
patients and trials? How to explicitly handle the difference between inclusion
and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse
network (COMPOSE) to address these challenges for patient-trial matching. One
path of the network encodes EC using convolutional highway network. The other
path processes EHR with multi-granularity memory network that encodes
structured patient records into multiple levels based on medical ontology.
Using the EC embedding as query, COMPOSE performs attentional record alignment
and thus enables dynamic patient-trial matching. COMPOSE also introduces a
composite loss term to maximize the similarity between patient records and
inclusion criteria while minimize the similarity to the exclusion criteria.
Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria
matching and 83.7% accuracy on patient-trial matching, which leads 24.3%
improvement over the best baseline on real-world patient-trial matching tasks.
