A novel deep learning approach to extract Chinese clinical entities for lung cancer screening and staging.

Journal: BMC medical informatics and decision making
Published Date: Jul 30, 2021

Abstract

BACKGROUND: Computed tomography (CT) reports record a large volume of valuable information about patients' conditions and the interpretations of radiology images from radiologists, which can be used for clinical decision-making and further academic study. However, the free-text nature of clinical reports is a critical barrier to use this data more effectively. In this study, we investigate a novel deep learning method to extract entities from Chinese CT reports for lung cancer screening and TNM staging.

Authors

  • Huanyao Zhang
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Zheda Road, Hangzhou, China.
  • Danqing Hu
    College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
  • Huilong Duan
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.
  • Shaolei Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.
  • Xudong Lu
    The College of Biomedical Engineering and Instrument Science, Zhejiang University, 310027 Hangzhou, Zhejiang, China.

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