LCD benchmark: long clinical document benchmark on mortality prediction for language models
We expect our LCD benchmark to be a resource for the development of advanced supervised models, or prompting methods, tailored for clinical text.
Authors
- Zhanzhan Zhao
- Wonjin Yoon (ORCID: https://orcid.org/0000-0002-6435-548X)
- Danielle S. Bitterman (ORCID: https://orcid.org/0000-0003-0345-2232)
- Timothy A. Miller (ORCID: https://orcid.org/0000-0003-4513-403X)
- Dmitriy Dligach (ORCID: https://orcid.org/0000-0002-2585-2707)
- Majid Afshar (ORCID: https://orcid.org/0000-0002-6368-4652)
- Shan Chen (ORCID: https://orcid.org/0000-0001-7999-7410)
- Yanjun Gao (ORCID: https://orcid.org/0000-0002-9341-7360)
Institutions
- Brigham and Women's Hospital (US)
- Boston Children's Hospital (US)
- University of Wisconsin–Madison (US)
- Harvard University (US)
- Loyola University Chicago (US)
- Dana-Farber Cancer Institute (US)
- Dana-Farber Brigham Cancer Center (US)
- Mass General Brigham (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2024-11-02
- DOI
- https://doi.org/10.1093/jamia/ocae287
- Citations
- 6
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- Korea University
- National Institutes of Health