LCD benchmark: long clinical document benchmark on mortality prediction for language models.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Feb 1, 2025

Abstract

OBJECTIVES: The application of natural language processing (NLP) in the clinical domain is important due to the rich unstructured information in clinical documents, which often remains inaccessible in structured data. When applying NLP methods to a certain domain, the role of benchmark datasets is crucial as benchmark datasets not only guide the selection of best-performing models but also enable the assessment of the reliability of the generated outputs. Despite the recent availability of language models capable of longer context, benchmark datasets targeting long clinical document classification tasks are absent.

Authors

  • Wonjin Yoon
    Department of Computer Science and Engineering, Korea University, Seoul, 02841, Republic of Korea.
  • Shan Chen
    National Academy of Economic Security, Beijing Jiaotong University, Beijing 100044, China.
  • Yanjun Gao
    Department of Biomedical Informatics, University of Colorado-Anschutz Medical, Aurora, CO 80045, United States.
  • Zhanzhan Zhao
    Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02215, United States.
  • Dmitriy Dligach
    Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
  • Danielle S Bitterman
    Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
  • Majid Afshar
    Loyola University Chicago, Chicago, IL.
  • Timothy Miller
    School of Computing and Information Systems, University of Melbourne, Victoria 3010, Australia.

Keywords

External Resources

Popular Topics
Recent Journals