Comparative analysis of machine learning approaches for predicting frequent emergency department visits
Bibliographic record
Abstract
BACKGROUND: Emergency Department (ED) overcrowding is an emerging risk to patient safety. This study aims to assess and compare the predictive ability of machine learning (ML) models for predicting frequent ED users. METHOD: Korean Health Panel data from 2008 to 2015 were used for this study. Individuals with four or more visits per year were considered frequent ED users. Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM) as well as two ensemble models, namely Bagging and Voting, were trained and tested to examine their predictive performance. RESULTS: The ML classification algorithms identified frequent ED users with high precision (90%-98%) and sensitivity (87%-91%), whereas LR showed fair precision (65%) and sensitivity (67%). The ML algorithms showed a high area under the curve (AUC) values from 89% for SVM to 96% for Random Forest, while LR showed the lowest AUC (65%). The classification error varied among algorithms; LR had the highest classification error (24.07%) while RF had the least (3.8%). CONCLUSIONS: Results show that ML classification algorithms are robust techniques to predict frequent ED users, and the variables in administrative health panels are reliable indicators for this purpose.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".