OLR-Net: Object Label Retrieval Network for principal diagnosis extraction.

Journal: Computers in biology and medicine
Published Date: Sep 16, 2024

Abstract

BACKGROUND: Extracting principal diagnosis from patient discharge summaries is an essential task for the meaningful use of medical data. The extraction process, usually by medical staff, is laborious and time-consuming. Although automatic models have been proposed to retrieve principal diagnoses from medical records, many rare diagnoses and a small amount of training data per rare diagnosis provide significant statistical and computational challenges.

Authors

  • Kai Wang
    Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, Jiangsu, China.
  • Xin Tan
    School of Public Health, Chengdu Medical College, Chengdu 610500, China.
  • Shan Nan
    Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.
  • Lei Sang
    Hainan Hospital of Chinese People's Liberation Army General Hospital, Sanya 572013, China.
  • Han Chen
    School of Statistics, University of Minnesota at Twin Cities.
  • Huilong Duan
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.

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