Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

Journal: BMC medical informatics and decision making
Published Date: Nov 19, 2020

Abstract

BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.

Authors

  • Ahmed Abdulaal
    Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
  • Aatish Patel
    Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
  • Esmita Charani
    National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, Hammersmith Campus, Du Cane Road, London, W12 0NN, UK.
  • Sarah Denny
    Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
  • Saleh A Alqahtani
    King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
  • Gary W Davies
    Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
  • Nabeela Mughal
    Chelsea and Westminster NHS Foundation Trust, 369 Fulham Road, London, SW10 9NH, UK.
  • Luke S P Moore
    Health Protection Unit in Healthcare Associated infections and Antimicrobial Resistance, Imperial College London, 8th floor Commonwealth Building, Hammersmith Hospital Campus, Acton, London, W12 0NN, UK.

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