Predicting 30-Day Readmissions in Patients With Heart Failure Using Administrative Data: A Machine Learning Approach
Bibliographic record
Abstract
BACKGROUND: We sought to develop machine learning (ML) models trained on administrative data which predict risk of readmission in patients with heart failure and to evaluate and compare the ML model with the currently used LaCE score using clinically informative metrics. METHODS AND RESULTS: This prognostic study was conducted in Alberta, Canada, on 9845 patients with confirmed heart failure admitted to hospital between 2012 and 2019. The outcome was unplanned all-cause hospital readmission within 30 days of discharge. We used 80% of the data for the ML model development and 20% for independent validation. We reported, using the validation set, c-statistics (area under the receiver operating characteristic curves)and performance metrics (likelihood ratio, positive predictive values) for the XGBoost model and a modified LaCE score within their respective predictive thresholds. Boosted tree-based classifiers had higher area under the receiver operating characteristic curves (0.65 for XGBoost) compared with others (0.58 for neural networks) and 0.57 for the modified LaCE. Within the predicted threshold range of the XGBoost classifier, the positive likelihood ratio was 1.00 at the low end of predicted risk and 6.12 at the high end, resulting in a positive predictive value (post-test probability) range of 21%-62%; the pretest probability of readmission was 20.9% using prevalence. The corresponding positive likelihood ratios and positive predictive values across LaCE score thresholds were 1.00-1.20 and 21%-24%, respectively. CONCLUSIONS: Despite predicting readmissions better than the LaCE, even the best ML model trained on administrative health data (XGBoost) did not provide substantially informative prediction performance as it only generated a moderate shift from pre to post-test probability. Health systems wishing to deploy such a tool should consider training ML models with additional data. Adding other techniques like natural language processing, along with ML, to use other clinical information (like chart notes) might improve prediction performance.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".