Emergency Department Wait Time Forecast based on Semantic and Time Series Patterns in COVID-19 Pandemic
Bibliographic record
Abstract
This study introduces a new ensemble architecture to improve the wait time forecast for healthcare service in the emergency department (ED) of hospital. The new model first used a fine-tuned text embedding model to extract the contextual semantic meaning of patients’ chief complaint from the electronic patient records to estimate the degree of case urgency and combined to a recurrent neural network to process the regular ED wait time patterns. Four text embedding models including the universal sentence encoder with DAN and transformer encoders, the NNLM, and the Swivel were used for semantic analysis. The results show that the new ensemble model can reduce the prediction errors maximumly by 20.0% in mean of absolute error (MAE), 46.0% in mean of squared error (MSE), and 26.6% in root mean squared error (RMSE). A 5-fold cross validation verified that the new model is robust to the ED wait time prediction before and during the COVID-19 pandemic. We conclude that the new model provides an innovative approach to apply semantic analysis of natural language processing to the domain of time series prediction in the healthcare domain.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".