A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

Journal: Bioinformatics (Oxford, England)
Published Date: Jan 1, 2023

Abstract

MOTIVATION: In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.

Authors

  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Zhiwei Li
    Department of Instrument Science & Technology, Zhejiang University, Hangzhou, 310027, China.
  • Hongyi Fang
    School of Data Science, Fudan University, Shanghai 200433, China.
  • Qianyuan Yao
    School of Data Science, Fudan University, Shanghai 200433, China.
  • Cheng Zhong
    Lawrence Berkeley National Laboratory, Berkeley CA USA.
  • Jianye Hao
    College of Intelligence and Computing, Tianjin University, Peiyang Park Campus: No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, China. haojianye@gmail.com.
  • Qi Zhang
    Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xuanjing Huang
    School of Computer Science, Fudan University, 200433 Shanghai, China.
  • Jiajie Peng
    School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. jiajiepeng@hit.edu.cn.
  • Zhongyu Wei
    School of Data Science, Fudan University, Shanghai, China.

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