Artificial intelligence to predict the risk of mortality from Covid-19: Insights from a Canadian Application

Abstract

The Severe Acute Respiratory Syndrome COVID-19 virus (SARS-CoV-2) has had enormous impacts, indicating need for non-pharmaceutical interventions (NPIs) using Artificial Intelligence (AI) modeling. Investigation of AI models and statistical models provides important insights within the province of Ontario as a case study application using patients' physiological conditions, symptoms, and demographic information from datasets from Public Health Ontario (PHO) and the Public Health Agency of Canada (PHAC). The findings using XGBoost provide an accuracy of 0.9056 for PHO, and 0.935 for the PHAC datasets. Age is demonstrated to be the most important variable with the next two variables being Hospitalization and Occupation. Further, AI models demonstrate identify the importance of improved medical practice which evolved over the six months in treating COVID-19 virus during the pandemic, and that age is absolutely now the key factor, with much lower importance of other variables that were important to mortality near the beginning of the pandemic. An XGBoost model is shown to be fairly accurate when the training dataset surpasses 1000 cases, indicating that AI has definite potential to be a useful tool in the fight against COVID-19 even when caseload numbers needed for effective utilization of AI model are not large.

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

“…Likewise, there is a great variety of studies of this type despite the short time that has elapsed since the pandemic began [ 16 , 41 ]. We can find research around the world about this topic employing IA techniques such as random forest models [ 17 , 42 , 43 , 44 , 45 ], deep learning [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ], decision trees [ 43 , 54 ], support vector machine (SVM) [ 49 , 55 ] and logistic regression procedures [ 49 , 56 ]; which are intended to predict the health status (mortality risk or disease severity) of a COVID-19 infected patient employing factors such as the patients age, weight, gender, physiological conditions, demographic data, travel data, computed tomography, vital signs, symptoms, smoking history, radiological features, clinical features, genetic variants, platelets, laboratory test, D-dimer test, chronic comorbidities and general health information. Meantime, other studies [ 57 ] create models using data analysis techniques with the aim of predicting the need of oxygen therapy in a timely manner in COVID-19 patients; which employed variables like shortness of breath, cough, age and fever.…”

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“…Year Accuracy Cohen et al [36] 2020 80% Amer et al [37] 2020 94% Afshar et al [38] 2020 96.24% Borkowski et al [39] 2020 89% Harmon et al [40] 2020 90.8% Snider et al [41] 2020 90.56% Proposed method 2021 91%…”