Forecasting Emergency Department Arrivals via \nRegression with ARIMA errors and Facebook Prophet: \nThe Case of a Montreal Hospital
Bibliographic record
Abstract
This thesis is motivated by a practical problem in emergency department (ED) operations management. Prolonged waiting times and overcrowding are prevalent in EDs as a result of the mismatch between demand (i.e., patient arrivals) and supply of ED services (Morley, Unwin, Peterson, Stankovich, & Kinsman, 2018). As the gateway to modern healthcare systems, EDs are faced with the arrival of patients with urgent and complex care needs, increased arrivals of the elderly, and high volume of low-acuity patient arrivals. To improve the operational efficiency and healthcare delivery, ED administrators have to make informed decisions about efficient allocation of resources; demand forecasting is a first step towards informing such decisions. Using a Montreal hospital ED as a basis for our investigation, we evaluate the effectiveness of the rarely used regression with autoregressive integrated moving average errors (regARIMA) model in forecasting future daily and hourly ED arrivals. We also experimentally evaluate the performance of Facebook Prophet (fbprophet) and demonstrate its competitiveness with established forecasting methods. This insight is particularly valuable given that in the ED arrival forecasting literature, it is viewed as a “Blackbox” or “off-the-shelf” method and has not been used for comparison with other established methods. Furthermore, we investigate the hypothesis that public sporting events, particularly hockey, lead to increased arrivals to the ED by using hockey games as a predictor within our forecasting models.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".