Exploration of text matching methods in Chinese disease Q&A systems: A method using ensemble based on BERT and boosted tree models.

Journal: Journal of biomedical informatics
Published Date: Jan 20, 2021

Abstract

BACKGROUND: Text matching is one of the basic tasks in the field of natural language processing. Owing to the particularity of Chinese language and medical texts, text matching has greater application and research value in the medical field. In 2019, at the China Health Information Processing Conference (CHIP), 30,000 sets of real disease Q&A data in Chinese on diabetes, hypertension, hepatitis B, AIDS, and breast cancer were released for public evaluation. A total of 90 teams participated in the evaluation.

Authors

  • Ziming Wu
    WeChat, Tencent Inc., Guangzhou, Guangdong Province, China. Electronic address: zimingwu@tencent.com.
  • Jun Liang
    Department of AI and IT, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.
  • Zhongan Zhang
    Performance Management Department, Qingdao Central Hospital, Qingdao, Shandong Province, China. Electronic address: zza6688@sina.com.
  • Jianbo Lei
    Clinical Research Center, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.

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