  Substantial increase in the use of Electronic Health Records (EHRs) has
opened new frontiers for predictive healthcare. However, while EHR systems are
nearly ubiquitous, they lack a unified code system for representing medical
concepts. Heterogeneous formats of EHR present a substantial barrier for the
training and deployment of state-of-the-art deep learning models at scale. To
overcome this problem, we introduce Description-based Embedding, DescEmb, a
code-agnostic description-based representation learning framework for
predictive modeling on EHR. DescEmb takes advantage of the flexibility of
neural language understanding models while maintaining a neutral approach that
can be combined with prior frameworks for task-specific representation learning
or predictive modeling. We tested our model's capacity on various experiments
including prediction tasks, transfer learning and pooled learning. DescEmb
shows higher performance in overall experiments compared to code-based
approach, opening the door to a text-based approach in predictive healthcare
research that is not constrained by EHR structure nor special domain knowledge.
