Creating a guide to identify patients who may leave without being seen: A machine learning approach
Bibliographic record
Abstract
Patients and their caregivers who seek care in an Emergency Department (ED) may ultimately choose to leave without being seen by a physician. This occurrence is labeled “left without being seen” (LWBS) and can account for up to 15% of all patients who come to an ED. Patients who LWBS do not receive the care they seek in the ED and may experience clinical deterioration related to delayed diagnosis or treatment. Identifying which patients are more likely to become LWBS patients (and intervening) could prevent adverse outcomes related to LWBS. This paper aims to create a paper-based guide to identify patients at risk of LWBS proactively. The emphasis is on creating a guide that can be easily used by staff in real time with readily available data. The most important features for predicting LWBS are determined using descriptive statistics and SHAP value analysis. The typical ranges for those features are then analyzed with a trained machine-learning model to determine feature combinations that lead to LWBS. Threshold values for these feature combinations are then determined to form the foundation for the guide designed to fit a single piece of paper. The guide was developed using data from the Pediatric Emergency Department at IWK Health in Halifax, Nova Scotia, Canada.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".