Development and internal validation of an AI-based emergency triage model for predicting critical outcomes in emergency department
Bibliographic record
Abstract
Emergency department (ED) overcrowding contributes to delayed patient care and worse clinical outcomes. Traditional triage systems face accuracy and consistency limitations. This study developed and internally validated a machine learning model predicting intensive care unit (ICU) admissions and resource utilization in ED patients. A retrospective analysis of 163,452 ED visits (2018-2022) from Maharaj Nakhon Chiang Mai Hospital evaluated logistic regression, random forest, and XGBoost models against the Canadian Triage and Acuity Scale (CTAS). The XGBoost model achieved superior predictive performance (AUROC 0.917 vs. 0.882, AUPRC 0.629 vs. 0.333). Key predictors included mode of arrival, patient age, and free-text chief complaints analyzed with multilingual sentence embeddings. These results demonstrate that machine learning, incorporating unstructured text data, has the potential to enhance triage accuracy and resource allocation by more effectively identifying critically ill patients compared to traditional triage methods.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".