Digital Phenotyping of Rare Endocrine Diseases Across International Data Networks and the Effect of Granularity of Original Vocabulary
Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.
Authors
- Geonah Kim (ORCID: https://orcid.org/0009-0008-5373-133X)
- Sarah Seager (ORCID: https://orcid.org/0000-0002-0614-4166)
- Namki Hong (ORCID: https://orcid.org/0000-0002-8246-1956)
- Seng Chan You (ORCID: https://orcid.org/0000-0002-5052-6399)
- Yumie Rhee (ORCID: https://orcid.org/0000-0003-4227-5638)
- Sin Gon Kim (ORCID: https://orcid.org/0000-0002-7430-3675)
- Jae Hyun Bae (ORCID: https://orcid.org/0000-0002-1384-6123)
- Kyoung Jin Kim (ORCID: https://orcid.org/0000-0001-7925-2515)
- Seunghyun Lee (ORCID: https://orcid.org/0000-0001-9254-183X)
- Xia Li (ORCID: https://orcid.org/0000-0002-9794-2648)
- Sungjae Shin (ORCID: https://orcid.org/0000-0003-3213-580X)
- Jing Li
Institutions
- Yonsei University (KR)
- Korea University (KR)
- Seoul National University Hospital (KR)
- IQVIA (United Kingdom) (GB)
- IQ Solutions (US)
Publication Details
- Journal
- Yonsei Medical Journal
- Published
- 2024-12-04
- DOI
- https://doi.org/10.3349/ymj.2023.0628
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- Korea Health Industry Development Institute