Mining hospital admission-discharge data to discover the chance of readmission
Bibliographic record
Abstract
The rising cost of unplanned hospital readmissions has sparked calls for identifying medical system failures, best practices, and interventions in order to reduce the incidence of avoidable readmission. Readmissions currently account for 18% of total hospital admissions among Medicare patients in the United States. Distinguishing avoidable from unavoidable readmissions is a complex problem, but tackling it can shed light on readmission determinants and contributing factors. The objective of this thesis is to gain knowledge about the role that dispensed drugs, medical procedures, and diagnostic information play in predicting the chance of readmission within thirty days from a hospital discharge, using machine learning techniques. The prediction of hospital readmission is formulated as a supervised learning problem. Two supervised learning models, Naïve Bayes and Decision Tree, are used in the thesis to predict the chance of readmission based on patients' demographic information, prescription drugs, diagnosis and procedure codes extracted from hospital discharge summaries. The empirical analysis improves the understanding of hospital readmission prediction and identifies patient subpopulations for which the readmission prediction is naturally more difficult. Comparing the performance of different methods, using AUC as the measure of performance, we found that the combination of Naïve Bayes classifier and Gini Index feature selection performs slightly better than other methods on this dataset. We also found that some diagnostic features play an important role in distinguishing outliers. Removing outliers from the entire data results in significant performance gains in the prediction of readmission.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".