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

• URL: http://arxiv.org/abs/2111.09098v1
• Date: Fri, 12 Nov 2021 20:27:55 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 introduceDescription-based Embedding,DescEmb, a code-agnostic representation learning framework for EHR. DescEmb takes advantage of the flexibil-ity of neural language understanding models toembed clinical events using their textual descrip-tions rather than directly mapping each event to a dedicated embedding.
• Score: 7.3394352452936085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals. To overcome this problem, we introduceDescription-based Embedding,DescEmb, a code-agnostic representation learning framework forEHR. DescEmb takes advantage of the flexibil-ity of neural language understanding models toembed clinical events using their textual descrip-tions rather than directly mapping each event toa dedicated embedding. DescEmb outperformedtraditional code-based embedding in extensiveexperiments, especially in a zero-shot transfertask (one hospital to another), and was able totrain a single unified model for heterogeneousEHR datasets.

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