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 h...
详细信息
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 h...
详细信息
ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
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.
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