Detection of medical text semantic similarity based on convolutional neural network.

Journal: BMC medical informatics and decision making
Published Date: Aug 7, 2019

Abstract

BACKGROUND: Imaging examinations, such as ultrasonography, magnetic resonance imaging and computed tomography scans, play key roles in healthcare settings. To assess and improve the quality of imaging diagnosis, we need to manually find and compare the pre-existing reports of imaging and pathology examinations which contain overlapping exam body sites from electrical medical records (EMRs). The process of retrieving those reports is time-consuming. In this paper, we propose a convolutional neural network (CNN) based method which can better utilize semantic information contained in report texts to accelerate the retrieving process.

Authors

  • Tao Zheng
    Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China; Key Laboratory of Renewable Energy, Chinese Academy of Sciences, Guangzhou 510640, People's Republic of China. Electronic address: zhengtao@ms.giec.ac.cn.
  • Yimei Gao
    Synyi Research, Shanghai, China.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Chenhao Fan
    Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Xingzhi Fu
    Tongren Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Mei Li
    Department of Laboratory Medicine, Med+X Center for Manufacturing, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
  • Ya Zhang
    Department of Plant Protection, College of Plant Protection, Hunan Agricultural University, Changsha, China. Electronic address: zhangya230@126.com.
  • Shaodian Zhang
    Biomedical Informatics, Columbia University, New York, NY, USA.
  • Handong Ma
    Synyi Co. Ltd., Shanghai, China; Department of Computer Science, Shanghai Jiao Tong University, Shanghai, China.

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