Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
Proceedings of the Conference on Health, Inference, and Learning, PMLR 174:183-203, 2022.
Increase in the use of Electronic Health Records (EHRs) has facilitated advances in predictive healthcare. However, EHR systems lack a unified code system for representing medical concepts. Heterogeneous formats of EHR present a 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 models while maintaining a neutral approach that can be combined with prior frameworks for task-specific representation learning or predictive modeling. We test our model’s capacity on various experiments including prediction tasks, transfer learning and pooled learning. DescEmb shows higher performance in overall experiments compared to the 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.