ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

Journal: BMC medical informatics and decision making
Published Date: Jul 16, 2024

Abstract

BACKGROUND: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based on the Berlin definition. We also aim to develop a visualization tool that helps clinicians efficiently assess ARDS criteria.

Authors

  • Amir Gandomi
    Northwell, New Hyde Park, NY, USA; Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Frank G. Zarb School of Business, Hofstra University, Hempstead, NY, USA.
  • Phil Wu
    AiD Technologies, Stony Brook, NY, USA.
  • Daniel R Clement
    Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
  • Jinyan Xing
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Rachel Aviv
    Kaiser Permanente, Oakland, CA, USA.
  • Matthew Federbush
    Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY, USA.
  • Zhiyong Yuan
    School of Computer Science, Wuhan University, Wuhan, 430072, China.
  • Yajun Jing
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Guangyao Wei
    Department of Critical Care Medicine, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Negin Hajizadeh
    Institute of Health System Science, Feinstein Institute for Medical Research, Manhasset, NY, USA.

Keywords

External Resources

Popular Topics
Recent Journals