Hybrid triaging assistance algorithm for continuous patient monitoring
Bibliographic record
Abstract
Objective: This study aims to develop and evaluate a transformer-based neural network model that leverages both vital signs and chief complaints to predict patient acuity more accurately and automatically. This study is a part of a major project, which envisions continuous monitoring in the emergency department, while this model provides a machine learning based tool to risk stratify patients. Methods: This study utilized the public MIMIC-IV-ED dataset, containing patients' vital signs, chief complaints, and triage acuity levels. We developed multiple machine learning models, including a baseline model using only vital signs and a hybrid model that contains the transformer architecture and feed-forward neural networks, incorporating numerical and textual data types. A secondary analysis was performed after filtering inconsistent data points to test the model in an idealized scenario. Results: Models incorporating chief complaints achieved significantly higher accuracy (over 70%) compared to baseline models that relied solely on vital signs (around 60%). After filtering out inconsistent data, the hybrid model's accuracy improved to over 90%. Conclusion: Integrating chief complaints is critical for improving the accuracy of AI-driven triage models. These findings highlight the potential for hybrid systems to enhance patient monitoring and prevent deterioration in ED waiting rooms. Future work should focus on incorporating more diverse datasets and time series data to further validate and improve model performance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".