Automatic quantitative stroke severity assessment based on Chinese clinical named entity recognition with domain-adaptive pre-trained large language model.

Journal: Artificial intelligence in medicine
Published Date: Feb 27, 2024

Abstract

BACKGROUND: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest.

Authors

  • Zhanzhong Gu
    School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia. Electronic address: zhanzhong.gu@student.uts.edu.au.
  • Xiangjian He
    School of Electrical and Data Engineering (SEDE), University of Technology Sydney, 2007, Sydney, Australia.
  • Ping Yu
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, the Chinese Academy of Sciences (CAS), Beijing 100190, China.
  • Wenjing Jia
    School of Electrical and Data Engineering (SEDE), University of Technology Sydney, 2007, Sydney, Australia.
  • Xiguang Yang
    School of Electrical and Data Engineering, University of Technology Sydney, NSW, 2007, Australia.
  • Gang Peng
    Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100193, China.
  • Penghui Hu
    Department of Oncology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Shiyan Chen
    Department of Ophthalmology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu, China.
  • Hongjie Chen
    Department of Civil & Environmental Engineering, National University of Singapore, E1A-07-03, 1 Engineering Drive 2, 117576, Singapore.
  • Yiguang Lin
    School of Life Sciences, University of Technology Sydney, Sydney, Australia. Electronic address: Yiguang.lin@uts.edu.au.

Keywords

External Resources

Popular Topics
Recent Journals