How Good Is Machine Learning in Predicting All-Cause 30-Day Hospital Readmission? Evidence From Administrative Data
Bibliographic record
Abstract
OBJECTIVES: Hospital readmission is a main cost driver for healthcare systems, but existing works often had poor or moderate predictive results. Although the available information differs in different studies, improving prediction is different from the search for important explanatory variables. With large sample size and abundant information, this study explores state-of-the-art machine-learning algorithms and shows their performance in prediction. METHODS: Using administrative data on 1 631 611 hospital stays from Quebec between 1995 and 2012, we predict the probability of 30-day readmission at hospital admission and discharge. We compare the performance between traditional logistic regression, logistic regression with penalization, and more recent machine-learning algorithms such as random forest, deep learning, and extreme gradient boosting. RESULTS: After a 10-fold cross-validation on the training set (80% of the data), machine learning produced very good results on a separate hold-out test set (20% of the data). The importance of explanatory variables is not the same for different algorithms. The area under receiver operating characteristic curve (AUC) reached above 0.79 at hospital admission and above 0.88 at hospital discharge. Diagnostic codes, which include many different categories, are among the most predictive variables. Logistic regression with penalization also produced good results, but a standard logistic regression failed without penalization. The good results are confirmed by calibration curves. CONCLUSION: Although the identification of those at highest risk of readmission is just 1 step to preventing hospital readmissions, 30-day readmission is highly predictable with machine learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".