2021/08/08 by Kyunghoon Hur, Hur, Kyunghoon, Jiyoung Lee +9 · 4 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2108.03625
openalex publication_date 2021/08/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.