Predicting Hospital Re-admissions from Clinical Narratives
Bibliographic record
Abstract
In this era, hospital re-admissions have been a significant concern as the numbers of readmissions are increasing at an alarming rate worldwide.The central idea of this paper is to predict unplanned patient re-admissions within 30 days of discharge.When a patient is admitted to a healthcare center, there are high chances of re-admissions based on many healthcare parameters.This paper proposes a Machine Learning-based K-Nearest Neighbor model to predict 30-day unplanned hospital re-admission using clinical notes.The extracted dataset will undergo various text pre-processing stages to improve the model's overall accuracy.To validate our proposed model, we have implemented many other Machine Learning models to compare different parameters obtained from each model.Hyperparameter tuning techniques and feature extraction techniques have been implemented to study the prediction results.According to our observations, the K-Nearest Neighbor model got the best accuracy of 85 percent, while logistic regression did not provide high accuracy.In this way, clinicians can intervene in patients' conditions beforehand, predict possible readmission chances, and take various precautionary treatment steps to avoid unplanned readmissions.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".