Transfer learning with chest X-rays for ER patient classification.

Journal: Scientific reports
Published Date: Dec 1, 2020

Abstract

One of the challenges with urgent evaluation of patients with acute respiratory distress syndrome (ARDS) in the emergency room (ER) is distinguishing between cardiac vs infectious etiologies for their pulmonary findings. We conducted a retrospective study with the collected data of 171 ER patients. ER patient classification for cardiac and infection causes was evaluated with clinical data and chest X-ray image data. We show that a deep-learning model trained with an external image data set can be used to extract image features and improve the classification accuracy of a data set that does not contain enough image data to train a deep-learning model. An analysis of clinical feature importance was performed to identify the most important clinical features for ER patient classification. The current model is publicly available with an interface at the web link: http://nbttranslationalresearch.org/ .

Authors

  • Jonathan Stubblefield
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Mitchell Hervert
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Jason L Causey
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Jake A Qualls
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Wei Dong
    Department of Cardiology, Chinese PLA General Hospital, Beijing, China.
  • Lingrui Cai
    Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.
  • Jennifer Fowler
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Emily Bellis
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA.
  • Karl Walker
    Department of Mathematics and Computer Science, University of Arkansas At Pine Bluff, Pine Bluff, AR, 55455, USA.
  • Jason H Moore
    University of Pennsylvania, Philadelphia, PA, USA.
  • Sara Nehring
    Center for No-Boundary Thinking (CNBT) at Arkansas State University, The Joint Translational Research Lab of Arkansas State University, St. Bernards Medical Center, Jonesboro, AR, 72467, USA. smnehring@sbrmc.org.
  • Xiuzhen Huang
    Department of Computer Science, Arkansas State University, Jonesboro, AR, USA.

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