SNOMED CT entity linking challenge
Findings from this work suggest that future projects should focus on improving entity linking for rare concepts and developing methods to better leverage contextual information when training examples are scarce.
Authors
- Chen Yanover (ORCID: https://orcid.org/0000-0003-3663-4286)
- Mihai Horia Popescu (ORCID: https://orcid.org/0000-0003-3378-0368)
- Rory Davidson (ORCID: https://orcid.org/0000-0001-8768-0288)
- Guy Amit (ORCID: https://orcid.org/0000-0002-4404-0683)
- Vincenzo Della Mea (ORCID: https://orcid.org/0000-0002-0144-3802)
- Irena Girshovitz (ORCID: https://orcid.org/0000-0002-6397-9055)
- Yonatan Bilu (ORCID: https://orcid.org/0000-0001-9119-7889)
- Kevin Roitero (ORCID: https://orcid.org/0000-0002-9191-3280)
- Aleksandr Galaida
- Gleb Sokolov
- Will Hardman
Institutions
- University of Udine (IT)
- London International Development Centre (GB)
- IMI TAMI Institute for Research and Development (IL)
- Kishwaukee College (US)
- WWF-UK (GB)
- Visio (NL)
- Center of Medical Genetics and Primary Health Care (AM)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1093/jamia/ocaf104
- Citations
- 4
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 3.22