Machine Learning Methods of Risk Evaluation for Community-acquired Clostridioides difficile Infection in Ontario
Bibliographic record
Abstract
Clostridioides difficile infection (CDI) is one of the leading causes of morbidity and mortality in Canada. Typically it is seen as a hospital-acquired infection, but growing numbers of community-acquired CDI and a high risk of recurrent infections has many researchers shifting their focus to the analysis of risk factors associated with community-acquired CDI. However, many of these studies only apply logistic regression for risk prediction techniques despite evidence that other machine learning methods, such as gradient boosted trees, can effectively predict risk of communicable diseases. This study focuses on the applications of three modeling techniques — logistic regression models, mixed effects models, and a gradient boosted tree model — in an effort to evaluate different risk factors associated with community-acquired CDI. Results indicate that taking multiple antibiotics concurrently, particularly if at least one of those antibiotics is classified as high-risk, is likely to significantly increase one’s probability of developing CDI. Additionally, results of the gradient boosted tree indicate that there are important temporal factors associated with risk of CDI. Going forward, the information obtained about the different risk factors associated with community-acquired CDI should be utilized by physicians to lower the risk of infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".