Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic that Predict SARS-CoV-2 Infection: Machine-learning Approach.

Journal: The western journal of emergency medicine
Published Date: Mar 4, 2021

Abstract

INTRODUCTION: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains challenging to diagnose the disease in a timely fashion in the emergency department (ED). In this study we aimed to construct machine-learning (ML) models to predict severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection based on the clinical features of patients visiting an ED during the early COVID-19 pandemic.

Authors

  • Eric H Chou
    Baylor Scott & White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas.
  • Chih-Hung Wang
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Yu-Lin Hsieh
    Danbury Hospital, Department of Internal Medicine, Danbury, Connecticut.
  • Babak Namazi
    Baylor Scott & White Research Institute, Dallas, TX, USA.
  • Jon Wolfshohl
    Baylor Scott & White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas.
  • Toral Bhakta
    Baylor Scott & White All Saints Medical Center, Department of Emergency Medicine, Fort Worth, Texas.
  • Chu-Lin Tsai
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Wan-Ching Lien
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Ganesh Sankaranarayanan
    Department of Surgery, Baylor University Medical Center, 3500 Gaston Ave, Dallas, TX, 75246, USA. ganesh.sankaranarayanan@bswhealth.org.
  • Chien-Chang Lee
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Tsung-Chien Lu
    Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals