Using machine learning to gain insights into chronic disease multimorbidity: trends and patterns in British Columbia, Canada
Bibliographic record
Abstract
ObjectiveThe goal of this project is to explore novel ways to assess chronic disease multimorbidity (co-occurrence of two or more conditions) trends and patterns in the population of British Columbia (BC), Canada. ApproachThis study included linked data from the BC population (~5M individuals) from 2001/02 to 2019/20. We analysed 25 chronic conditions, including 7 primary cancer subtypes. We report multimorbidity (MM) incidence, prevalence, and most common disease combinations. Further we explore temporal MM disease patterns using directed network analyses and extracted data-driven disease clusters with an unsupervised machine learning algorithm. ResultsThe age-standardized incidence of MM stayed relatively stable over the study period for males and decreased for females, while prevalence increased to approximately 1 in 4 individuals in BC in 2019/20 (from 19% to 27% of females, from 15% to 22% of males). Disease networks and clusters varied significantly by sex and age group, this presentation will highlight select disease network and cluster findings and discuss their implications for chronic disease surveillance. ConclusionsThe prevalence of multimorbidity continues to rise in BC. Using advanced analytics to understand disease co-occurrence patterns provides new insights above and beyond traditional epidemiological metrics to support health system planning and prevention efforts. ImplicationsChronic disease surveillance and research have historically operated with a single disease focus, which is not patient-centered and does not adequately account for the reality of multimorbidity for many people. This project is laying the foundation for enhanced chronic disease surveillance and monitoring in BC.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".