An unsupervised machine learning approach for defining surge levels in emergency medical services
Bibliographic record
Abstract
A surge period occurs when demand significantly exceeds available capacity, creating operational strain in emergency medical services (EMS) and leading to measurable declines in system performance. Although surge levels are a critical metric for EMS operations, no established method exists for their objective definition. This study introduces a genetic algorithm-based unsupervised clustering model designed to define surge levels using EMS operational data. Unlike the National Emergency Department Overcrowding Scale, which depends on subjective assessments, the proposed approach objectively categorizes surge levels and supports regional customization through hyperparameter tuning and feature selection. The model's adaptability allows healthcare leaders to determine the desired number of surge-level categories and tailor the feature set to local operational needs. A case study in Nova Scotia, Canada, demonstrates the model's effectiveness, accurately identifying 88.96 % of busy periods with recall and precision of 96.49 % and 78.57 %, respectively. These results indicate that the approach provides a robust and flexible tool for defining surge levels, enabling data-driven decision-making in EMS system 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".