publication . Article . 2021

Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada

Brett Snider; Edward A. McBean; John Yawney; S. Andrew Gadsden; Bhumi Patel;
Open Access English
  • Published: 01 Jun 2021 Journal: Frontiers in Public Health, volume 9 (eissn: 2296-2565, Copyright policy)
  • Publisher: Frontiers Media S.A.
The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and 90+. Findings result...
Persistent Identifiers
free text keywords: Public Health, Brief Research Report, artificial intelligence, XGBoost, SHapley, COVID-19, mortality, Public aspects of medicine, RA1-1270
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