Explainable machine learning to identify risk factors for unplanned hospital readmissions in Nova Scotian hospitals
Bibliographic record
Abstract
OBJECTIVE: A report from the Canadian Institute for Health Information found unplanned hospital readmissions (UHR) common, costly, and potentially avoidable, estimating a $1.8 billion cost to the Canadian healthcare system associated with inpatient readmissions within 30 days of discharge for the studied period (11 months). The first step towards addressing this costly problem is enabling early detection of patients at risk through detecting UHR risk factors. METHODOLOGY: We utilized Machine Learning and explainability tools to examine risk factors for UHR within 30 days of discharge, utilizing data from Nova Scotian (Canada) healthcare institutions (2015-2022). To the best of our knowledge, our research constitutes the most comprehensive study on UHR risk factors for the province. RESULTS: We found that predicting UHR solely from healthcare data has limitations, as discharge information often falls short of accurately predicting readmission occurrences. However, despite this inherent limitation, integrating explainability tools offers insights into the underlying factors contributing to readmission risk, empowering medical personnel with information to improve patient care and outcomes. As part of this work, we identify and report risk factors for UHR and build a guideline to support medical personnel's decision-making regarding targeted post-discharge follow-ups. We found that conditions such as heart failure and Chronic Obstructive Pulmonary Disease (COPD) are associated with a higher likelihood of readmission. Patients admitted for procedures related to childbirth have a lower probability of readmission. We studied the impact of the admission type, patient characteristics, and patient stay characteristics on UHR. For example, we found that new and elective admission patients are less likely to be readmitted, while patients who received a transfusion are more likely to be readmitted. CONCLUSIONS: We validated the risk factors and the guidelines using real-world data. Our results suggested that our proposal correctly identifies risk factors and effectively produces valuable guidelines for medical personnel. The guideline evaluation suggests we can screen half the patients while capturing more than 72% of the readmission episodes. Our study contributes insights into the challenge of identifying risk factors for UHR while providing a practical guideline for healthcare professionals to identify factors influencing patient readmission, particularly within Nova Scotia.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".