Automated ICD coding for primary diagnosis via clinically interpretable machine learning.

Journal: International journal of medical informatics
Published Date: Sep 1, 2021

Abstract

BACKGROUND: Computer-assisted clinical coding (CAC) based on automated coding algorithms has been expected to improve the International Classification of Disease, tenth version (ICD-10) coding quality and productivity, whereas studies oriented to primary diagnosis auto-coding are limited in the Chinese context.

Authors

  • Xiaolin Diao
    Department of Information Center, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 167 North Lishi Road, Xicheng District, Beijing, 100037, China.
  • Yanni Huo
    Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Shuai Zhao
    Xi'an Medical University, Xi'an Shaanxi, 710068, P.R.China.
  • Jing Yuan
    School of Acu-Mox and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Meng Cui
    Medical Record Department, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Yuxin Wang
    The Key Laboratory of Food Safety Risk Assessment, Ministry of Health, China National Center for Food Safety Risk Assessment, Beijing 100021, China. Electronic address: wangyx@cfsa.net.cn.
  • Xiaodan Lian
    Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.

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