TY - GEN
T1 - Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs
AU - Kollapally, Navya Martin
AU - Dehkordi, Mahshad Koohi H.
AU - Perl, Yehoshua
AU - Geller, James
AU - Deek, Fadi P.
AU - Liu, Hao
AU - Keloth, Vipina K.
AU - Elhanan, Gai
AU - Einstein, Andrew J.
AU - Zhou, Shuxin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Bio-ClinicalBERT
KW - BIOE tagging
KW - Cardiology clinical notes
KW - Clinical NER
KW - EHR notes
KW - Fast Skimming EHRs
KW - Highlighting
KW - Interface Terminology
KW - Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85217279036&partnerID=8YFLogxK
U2 - 10.1109/BIBM62325.2024.10822845
DO - 10.1109/BIBM62325.2024.10822845
M3 - Conference contribution
AN - SCOPUS:85217279036
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 6427
EP - 6434
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
Y2 - 3 December 2024 through 6 December 2024
ER -