Prediction of Patients’ Length of Stay at Hospital During COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Machine learning has been extensively used in diverse healthcare settings since the 21st century. Statistical models are proven to be powerful in detecting early disease symptoms and could potentially aid decision-making in the healthcare system. To help improve medical resource allocation during COVID-19 pandemic, we aim to develop machine learning models that predict each patient’s length of stay (LOS) in hospital. Three machine learning models, namely, K-nearest Neighbors Algorithm, Logistic Regression and Random Forest are implemented and optimized on the same healthcare dataset. The final accuracy of each model is 0.3442, 0.3524 and 0.3541 respectively, which are not very high. Our subsequent correlation analysis on the healthcare dataset shows the patients’ features used do not provide sufficient information for accurate LOS prediction. Yet, machine learning approaches could potentially yield much better results if the data quality can be improved by including additional relevant patient features and breaking LOS into more appropriate intervals. More detailed healthcare data should be obtained to make the LOS prediction useful for healthcare management.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".