Long-distance disorder-disorder relation extraction with bootstrapped noisy data.

Journal: Journal of biomedical informatics
Published Date: Aug 7, 2020

Abstract

OBJECTIVE: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-distance relation extraction algorithm that leverages the article section structure and is trained with bootstrapped noisy data to identify important relations for diagnosis, including may cause, may be caused by, and differential diagnosis.

Authors

  • Yucong Lin
    Center for Statistical Science, Tsinghua University, Beijing, Beijing, China; Department of Industrial Engineering, Tsinghua University, Beijing, Beijing, China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Keming Lu
    Department of Automation, Tsinghua University, Beijing, Beijing, China.
  • Cheng Ma
    Department of Statistics, University of Michigan, Ann Arbor, MI, USA.
  • Peng Zhao
    Department of Neurosurgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Daiqi Gao
    Department of Industrial Engineering, Tsinghua University, Beijing, Beijing, China.
  • Zihao Fan
    School of Information, University of California, Berkeley, CA, USA.
  • Zijie Cheng
    Department of Computer Science and Technology, Tsinghua University, Beijing, Beijing, China.
  • Zheyu Wang
    Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Sheng Yu
    Medical College, Guangxi University of Science and Technology, Liuzhou, Guangxi, 545005, China.

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