Classifying Emergency Patients into Fast-Track and Complex Cases Using Machine Learning
Bibliographic record
Abstract
Abstract Background: Emergency medicine is a lifeline specialty at hospitals that patients head to for various reasons, including serious health problems, traumas, and adventitious conditions. Emergency departments are restricted to limited resources and personnel, which complicates handling all received cases optimally. Therefore, crowded waiting areas and long waiting durations result, which prompts some patients to leave before being examined. Methods: We utilized the databases (MIMIC-IV-ED and MIMIC-IV) to obtain records of patients who visited the Beth Israel Deaconess Medical Center in the USA. Triage and demographic data, dispositions, and length of stay of these individuals were extracted accordingly. Subsequently, the urgency of these cases was inferred based on standards stated in the literature and followed in developed countries, which are less than four-hour lengths of stay besides being discharged at the end of the emergency visit. Five classifying models were established by utilizing logistic regression, random forests, stochastic gradient boosting, classification and regression trees besides multi-layer perceptron algorithms. Results: A comparative framework using the five different machine learning algorithms was developed to classify these patients into two categories where the multi-layer perceptron model outperformed the others. Moreover, the relative importance of the outcome predictors was determined. Conclusions: This study suggests an approach to deal with non-urgent visits and lower overall waiting times at the emergency by utilizing the powers of machine learning to identify fast-track patients and discern them from critical cases.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".