TriageIntelli: AI-Assisted Multimodal Triage System for Health Centers
Bibliographic record
Abstract
The overcrowding of the emergency departments presents a major challenge, exacerbated by an aging population and increasing complex cases. Triage, which prioritizes patients according to severity, faces significant pressure due to limited resources and growing patient numbers. This study explores the integration of artificial intelligence (AI) to enhance the triage process. We developed and evaluated AI-based models, including Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), Linear Regression (LR), XGBoost and a stacking model, to predict patient triage levels using the Korean Triage and Acuity Scale (KTAS). Our findings demonstrate that AI models, particularly SVM and GBM, delivered the highest prediction accuracies of 79% and 78.7%, respectively. These models also performed well in terms of precision (80.04% and 75.36%), recall (71.94% and 73.36%), and F1-score (72.93% and 72.91%). The remaining algorithms still demonstrated strong predictive capabilities. The developed Stacking Model exhibited the highest prediction, achieving an accuracy of 80.05%, precision of 80.27%, recall of 73.26%, and an F1-score of 74.41%. This incremental gain in performance demonstrates the effectiveness of model stacking, as it capitalizes on the complementary strengths of different algorithms to enhance overall predictive accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.003 |
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".