Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada
Bibliographic record
Abstract
The worldwide rapid spread of the severe acute respiratory syndrome coronavirus 2 has affected millions of individuals and caused unprecedented medical challenges by putting healthcare services under high pressure. Given the global increase in number of cases and mortalities due to the current COVID-19 pandemic, it is critical to identify predictive features that assist identification of individuals most at-risk of COVID-19 mortality and thus, enable planning for effective usage of medical resources. The impact of individual variables in an XGBoost artificial intelligence model, applied to a dataset containing 57,390 individual COVID-19 cases and 2,822 patient deaths in Ontario, is explored with the use of SHapley Additive exPlanations values. The most important variables were found to be: age, date of the positive test, sex, income, dementia plus many more that were considered. The utility of SHapley Additive exPlanations dependency graphs is used to provide greater interpretation of the black-box XGBoost mortality prediction model, allowing focus on the non-linear relationships to improve insights. A "Test-date Dependency" plot indicates mortality risk dropped substantially over time, as likely a result of the improved treatment being developed within the medical system. As well, the findings indicate that people of lower income and people from more ethnically diverse communities, face an increased mortality risk due to COVID-19 within Ontario. These findings will help guide clinical decision-making for patients with COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".