Abstract 14312: Comparison of Machine Learning and Conventional Statistical Modeling for Predicting Readmissions Following Acute Heart Failure Hospitalization
Bibliographic record
Abstract
Introduction: Developing accurate models for predicting risk of 30-day readmission has been a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. Objectives: To compare models developed using CSM or ML to predict 30-day readmission for cardiovascular and non-cardiovascular causes in HF patients. Methods: We studied 10,919 patients with HF (> 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada, linked to administrative databases for hospitalization and vital status resulting in complete follow-up. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests. Models were evaluated in the validation set using both discrimination and calibration metrics. Results: In the validation sample of 3602 patients (median age 76 [IQR, 67-82] years, 46.6% females), Random Survival Forests (c-statistic = 0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic= 0.621) for 30-day cardiovascular readmission. In contrast, for 30-day non-cardiovascular readmission, the Fine-Gray model (c-statistic= 0.641) slightly outperformed the random survival forests model (c-statistic = 0.632). For both outcomes, The Fine-Gray model displayed better calibration than random survival forests when deciles of observed vs. predicted risks were compared (Panels A-D). Conclusions: In HF patients, time-to-event analysis of outcomes using Fine-Gray models had similar discrimination but superior calibration to ML model, highlighting the importance of reporting calibration metrics for ML-based prediction models.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".