CACER: Clinical concept Annotations for Cancer Events and Relations
We introduce CACER, a novel corpus with fine-grained annotations for medical problems, drugs, and their relationships in clinical narratives of oncology notes. State-of-the-art transformer models achieved performance comparable to IAA for several extraction tasks.
Authors
- Meliha Yetişgen (ORCID: https://orcid.org/0000-0001-9919-9811)
- Giridhar Kaushik Ramachandran (ORCID: https://orcid.org/0000-0002-9800-6149)
- Ahmad Halwani (ORCID: https://orcid.org/0000-0001-6183-6491)
- Yujuan Fu (ORCID: https://orcid.org/0000-0002-6709-1539)
- Kevin Lybarger (ORCID: https://orcid.org/0000-0001-5798-2664)
- Özlem Uzuner (ORCID: https://orcid.org/0000-0001-8011-9850)
- Bridget T. McInnes (ORCID: https://orcid.org/0000-0003-2297-6672)
- Fei Xia (ORCID: https://orcid.org/0000-0003-4343-1444)
Institutions
- Center for Applied Linguistics (US)
- George Mason University (US)
- Virginia Commonwealth University (US)
- University of Washington (US)
- University of Utah (US)
- Huntsman Cancer Institute (US)
- University of Washington Medical Center (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-09-03
- DOI
- https://doi.org/10.1093/jamia/ocae231
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.47
Funders
- Johns Hopkins University
- National Institutes of Health
- National Cancer Institute
- U.S. National Library of Medicine