Factors Associated with Unplanned Hospital Readmission after Discharge: A Descriptive and Predictive Study Using Electronic Health Record Data
Bibliographic record
Abstract
Hospital readmission involves the unplanned emergency admission of patients within 30 days from discharge after the previous admission. According to the Canadian Health Institute (CIHI), 1 in 11 patients were readmitted within 30 days of leaving the hospital in 2021. In the USA, nearly 20% of Medicare patients were readmitted after discharge, where the average cost of readmission was approximately USD 15,000, as reported by the Agency for Healthcare Research and Quality (AHQR) in 2018. To tackle this issue, we first conducted a descriptive analysis study to understand the risk factors associated with hospital readmission, and then we applied machine learning approaches to predict hospital readmission by using patients’ demographic and clinical data extracted from the Electronic Health Record of the MIMIC-III clinical database. The results showed that the number of previous admissions during the last 12 months, hyperosmolar imbalance and comorbidity index were the top three significant factors for hospital readmission. The predictive model achieved a performance of 95.6% AP and an AUC = 97.3% using the Gradient Boosting algorithm trained on all features.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".