Unifying Heterogenous Electronic Health Records Systems via Text-Based Code Embedding

• URL: http://arxiv.org/abs/2108.03625v1
• Date: Sun, 8 Aug 2021 12:47:42 GMT
• Title: Unifying Heterogenous Electronic Health Records Systems via Text-Based Code Embedding
• Authors: Kyunghoon Hur, Jiyoung Lee, Jungwoo Oh, Wesley Price, Young-Hak Kim, Edward Choi
• Abstract summary: We introduce DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR. We tested our model's capacity on various experiments including prediction tasks, transfer learning and pooled learning.
• Score: 7.3394352452936085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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