Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Document Type

Conference Proceeding

Publication Date

1-1-2024

Abstract

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting.

Identifier

85217279036 (Scopus)

ISBN

[9798350386226]

Publication Title

Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

External Full Text Location

https://doi.org/10.1109/BIBM62325.2024.10822845

First Page

6427

Last Page

6434

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