ArticleFrontiers in public health2021
Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Artificial intelligence-driven prediction of COVID-19-related hospitalization and death: a systematic review.Frontiers in public health · 2023Pooled it
- Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.International journal of emergency medicine · 2026Article
- Metabolic dynamic score and machine learning: a novel approach to predicting pathological complete response in rectal cancer after neoadjuvant chemoradiotherapy.Frontiers in oncology · 2026Article
- Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study.International journal of colorectal disease · 2025Article
- Assessing the ecological resilience of Ebola virus in Africa and potential influencing factors based on a synthesized model.PLoS neglected tropical diseases · 2025Article
- Exploring the predictors affecting the sense of community of Korean high school students: application of random forests and SHAP.Frontiers in psychology · 2024Article
- COVID-19 Conceptual Modeling: Single-Center Cross-Sectional Study.JMIR formative research · 2023Article
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- Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data.Frontiers in artificial intelligence · 2023Article
- Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.PloS one · 2023Article
- Longitudinal Study of Therapeutic Adherence in a Cystic Fibrosis Unit: Identifying Potential Factors Associated with Medication Possession Ratio.Antibiotics (Basel, Switzerland) · 2022Article
- Machine learning based suicide prediction and development of suicide vulnerability index for US counties.Npj mental health research · 2022Article
- Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death.Frontiers in artificial intelligence · 2022Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Asymptomatic Cases, the Hidden Challenge in Predicting COVID-19 Caseload Increases.Infectious disease reports · 2021Article
- Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada.Frontiers in artificial intelligence · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions.
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