mlsanityData to 14 Oct 2026

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

arXiv 2111.09098 ↗First posted 12 Nov 2021cs.CL cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 12 Nov 2021.

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28 of 45 references.

References · 28

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16 more
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