Predicting COVID-19 Outcomes Among Albertans With Diabetes and COVID-19: A Machine Learning Approach
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Bibliographic record
Abstract
Background: Certain patients with diabetes and COVID-19 are at high risk of severe outcomes. Identification of risk factors among this group is required to risk-stratify those who may benefit from further surveillance. We aimed to develop machine learning (ML) models predicting severe outcomes among individuals with diabetes and COVID-19 in Alberta, Canada. Methods: Patients with diabetes and COVID-19 determined by PCR test administered in community and/or emergency department (ED) settings (March 2020-March 2021) were included. Outcomes were ED visit, hospitalization or death for those tested in the community (“Community cohort”) and hospitalization or death for those tested in ED (“ED cohort”), and in the combined cohorts (“Community+ED cohort”). Outcomes and features (sociodemographics, drug/healthcare utilization, health history) were identified using healthcare administrative data (2008-2021). Calibration plots, areas under the receiver operating curve, precision-recall curves (AUC, AUPRC), and threshold analyses were used to assess the models. Results: The Community cohort included 11,247 individuals (1,665 ED visits; 756 hospitalizations; 421 deaths). AUCs for models predicting ED/hospitalization/death were 0.65/0.70/0.93. The AUCs for predicting death in ED (1,495 individuals; 169 deaths) and Community+ED (12,410 individuals; 582 deaths) cohorts were 0.82 and 0.93. Models predicting hospitalization in these cohorts performed poorly and are not reported. Of all models, that predicting death from the Community performed best (sensitivity 0.77, specificity 0.91, positive predictive value 0.26, negative predictive value 0.99), and improved the prediction of death at a 10% risk threshold (compared to the pre-test probability, positive likelihood ratio 9.06 and negative likelihood ratio 0.25). Conclusion: Identifying diabetes patients at the highest risk of the worst outcomes would assist in triaging patients to ensure appropriate resource use in times of high demand. Overall, the model predicting death among patients with diabetes and COVID-19 in the community could be useful in identifying who requires additional care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 it