Incorporation of Korean Electronic Data Interchange Vocabulary into Observational Medical Outcomes Partnership Vocabulary

Seong, Yeonchan; You, Seng Chan; Ostropolets, Anna; Rho, Yeunsook; Park, Jimyung; Cho, Jaehyeong; Dymshyts, Dmitry; Reich, Christian; Heo, Yun-Jung; Park, Rae Woong

doi:10.4258/hir.2021.27.1.29

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…In the previous study, it was found that the integration of the EDI vocabulary into the OMOP vocabulary facilitates the standardization of EDI vocabulary per se. 6) …”

Section: Standardization Data Quality and Risk Of Re-identification O...mentioning

confidence: 99%

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

et al. 2022

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Author's summary A distributed research network refers to a research network wherein multiple institutions unite for joint research based on common data model wherein the structure and meaning of the data are standardized. Researchers can only send the analysis code to multiple institutions and get the summarized analysis results. Thus, researchers cannot see any of the individual patient data at any time, and no individual patient data can be leaked from the institutions. The Observational Health Data Sciences and Informatics research network standardized 928 million unique records or 12% of the world’s population from 41 countries.

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“…In the previous study, it was found that the integration of the EDI vocabulary into the OMOP vocabulary facilitates the standardization of EDI vocabulary per se. 6) …”

Section: Standardization Data Quality and Risk Of Re-identification O...mentioning

confidence: 99%

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

et al. 2022

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“…It has advantages as a data source in that it is structured and domestic standardized. However, EDI term has not been acknowledged as a standard vocabulary in the way that the Current Procedural Terminology, fourth edition has in the United States because its quality has never been audited 19 . Instead, there were several attempts to map the EDI term to SNOMED-CT 20 – 22 .…”

Section: Discussionmentioning

confidence: 99%

Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

Choi

Kim

Nam

et al. 2021

Sci Rep

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In recent years, there has been an emerging interest in the use of claims and electronic health record (EHR) data for evaluation of medical device safety and effectiveness. In Korea, national insurance electronic data interchange (EDI) code has been used as a medical device data source for common data model (CDM). This study performed a preliminary feasibility assessment of CDM-based vigilance. A cross-sectional study of target medical device data in EHR and CDM was conducted. A total of 155 medical devices were finally enrolled, with 58.7% of them having EDI codes. Femoral head prosthesis was selected as a focus group. It was registered in our institute with 11 EDI codes. However, only three EDI codes were converted to systematized nomenclature of medicine clinical terms concept. EDI code was matched in one-to-many (up to 104) with unique device identifier (UDI), including devices classified as different global medical device nomenclature. The use of UDI rather than EDI code as a medical device data source is recommended. We hope that this study will share the current state of medical device data recorded in the EHR and contribute to the introduction of CDM-based medical device vigilance by selecting appropriate medical device data sources.

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“…Despite many useful applications offered by concept normalizations, such as aligning data sources between different institutions, these opportunities are limited when such concept ontologies in non-English are limited or the local institution's text EHR is especially noisy. For example, despite many Korean medical communities' huge interest in mapping their local EHR to standardized concepts [28,[67][68][69][70], the Korean-translated version of LOINC was only recently released in 2022 [71]. Therefore, these preliminary key concepts generated from XLM-RoBERTa suggest an exciting potential role of transformerbased language models in building or expanding non-English text resources at the individual medical institution level.…”

Section: Lif T(w) =mentioning

confidence: 99%

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

Kim

Park

et al. 2023

IEEE Access

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As a key modifiable risk factor, alcohol consumption is clinically crucial information that allows medical professionals to further understand their patients' medical conditions and suggest appropriate lifestyle modifying interventions. However, identifying alcohol-related information from unstructured freetext clinical notes is often challenging. Not only are the formats of the notes inconsistent, but they also include a massive amount of non-alcohol-related information. Furthermore, for medical institutions outside of English-speaking countries, these clinical notes contain both a mixture of English and local languages, inducing additional difficulty in the extraction. Thanks to the increasing availability of electronic medical record (EMR), several previous works explored the idea of using natural language processing (NLP) to train machine learning models that automatically identify alcohol-related information from unstructured clinical notes. However, all these previous works are limited to English clinical notes, thereby able to leverage various large-scale external ontologies during the text preprocessing. Furthermore, they rely on simple NLP techniques such as the bag-of-words models that suffer from high dimensionality and out-ofvocabulary issues. Addressing these issues, we adopt fine-tuning multilingual transformers. By leveraging their linguistically rich contextual information learned during their pre-training, we are able to extract alcohol-related information from unstructured clinical notes without preprocessing the clinical notes on any external ontologies. Furthermore, our work is the first to explore the use of transformers in bilingual clinical notes to extract alcohol-related information. Even with minimal text preprocessing, we achieve extraction accuracy of 84.70% in terms of macro F-1 score. INDEX TERMS clinical informatics, alcohol information extraction, natural language processing, information extraction from clinical notes, multilingual transformers I. INTRODUCTIONAs medical institutions worldwide are widely adopting electronic medical record (EMR), vast amounts of healthcare data are produced and stored electronically [1-5]. As a significant component of EMR, clinical notes, which record patients' conditions in free text, provide essential information such as patients' medical history, social history, or lifestyle patterns. Despite being a vital data source, its practical use in medical decision support systems is hampered by challenges in extracting key information from its unstructured text format [6][7][8][9]. For medical institutions in non-English speaking countries, these challenges are compounded by the use of both English and local languages in their clinical notes. Besides standardizing the notes to resolve any inconsistent formats or structures, they need to handle the multilingual aspect of their notes simultaneously. Due to this additional

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Incorporation of Korean Electronic Data Interchange Vocabulary into Observational Medical Outcomes Partnership Vocabulary

Cited by 12 publications

References 11 publications

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

Establishment of an International Evidence Sharing Network Through Common Data Model for Cardiovascular Research

Preliminary feasibility assessment of CDM-based active surveillance using current status of medical device data in medical records and OMOP-CDM

Identifying Alcohol-Related Information From Unstructured Bilingual Clinical Notes With Multilingual Transformers

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