Predicting hospitalizations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study
Bibliographic record
Abstract
Abstract Objective To predict older adults’ risk of avoidable hospitalization related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. Design, Setting, and Participants A retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years, between 2008 and 2017. The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period. Data sources Administrative health data from Ontario, Canada obtained from the ICES Data Repository. Main outcome measures Risk of hospitalizations due to ACSCs one year after the observation period. Results The study used a total of 1,854,116 patients, split into train, validation, and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an AUC of 80.5% on the held-out test set, and the predictions were well-calibrated. When ranking the predictions made by the model, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalization. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalizations during the observation window, age, rural residence, and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in the province of Ontario, and especially the elevated risk in rural and marginalized regions. Conclusions This study aimed to predict the 1-year risk of hospitalization from a series of ambulatory-care sensitive conditions in seniors aged 65 to 74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalizations.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".