Machine learning prediction of premature death from multimorbidity among people with inflammatory bowel disease: a population-based retrospective cohort study
Bibliographic record
Abstract
Background: Multimorbidity, the co-occurrence of 2 or more chronic conditions, is important in patients with inflammatory bowel disease (IBD) given its association with complex care plans, poor health outcomes, and excess mortality. Our objectives were to describe premature death (age < 75 yr) among people with IBD and to identify patterns between multimorbidity and premature death among decedents with IBD. Methods: Using the administrative health data of people with IBD who died between 2010 and 2020 in Ontario, Canada, we conducted a population-based, retrospective cohort study. We described the proportion of premature deaths among people with IBD. We developed statistical and machine learning models to predict premature death from the presence of 17 chronic conditions and the patients’ age at diagnosis. We evaluated models using accuracy, positive predictive value, sensitivity, F1 scores, area under the receiver operating curve (AUC), calibration plots, and explainability plots. Results: All models showed strong performance (AUC 0.81–0.95). The best performing was the model that incorporated age at diagnosis for each chronic condition developed at or before age 60 years (AUC 0.95, 95% confidence interval 0.94–0.96). Salient features for predicting premature death were young ages of diagnosis for mood disorder, osteo-and other arthritis types, other mental health disorders, and hypertension, as well as male sex. Interpretation: By comparing results from multiple approaches modelling the impact of chronic conditions on premature death among people with IBD, we showed that conditions developed early in life (age ≤ 60 yr) and their age of onset were important for predicting their health trajectory. Clinically, our findings emphasize the need for models of care that ensure people with IBD have access to high-quality, multidisciplinary health care.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".