A temporal visualization of chronic obstructive pulmonary disease progression using deep learning and unstructured clinical notes.

Journal: BMC medical informatics and decision making
Published Date: Dec 17, 2019

Abstract

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that is classified into stages based on disease severity. We aimed to characterize the time to progression prior to death in patients with COPD and to generate a temporal visualization that describes signs and symptoms during different stages of COPD progression.

Authors

  • Chunlei Tang
    Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
  • Joseph M Plasek
    Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts.
  • Haohan Zhang
    Department of Mechanical Engineering, Columbia University, New York, New York, 10027.
  • Min-Jeoung Kang
    Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA. mkang6@bwh.harvard.edu.
  • Haokai Sheng
    Loomis Chaffee School, Windsor, CT, USA.
  • Yun Xiong
    Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China.
  • David W Bates
    Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
  • Li Zhou
    School of Education, China West Normal University, Nanchong, Sichuan, China.

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