Abstract 148: A Machine Learning-based Dispatch Rule for Drone-delivered Defibrillators
Bibliographic record
Abstract
Introduction: Drone-delivered defibrillators may reduce response time for out-of-hospital cardiac arrest (OHCA). However, an optimal dispatch rule is not yet known. Methods: We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2014 to Dec. 2019. We trained a neural network model to predict emergency medical services (EMS) response times using OHCA location, distance from responding ambulance, day of week and time of day. Instead of least-squares loss, our model optimized a loss function that penalized weighted errors in the dispatch decision (type I/II error). Assuming drones were deployed from three bases in the region, we calculated drone response time to each suspected OHCA using real drone specifications. Our dispatch rule dispatched a drone when its calculated response time was shorter than the predicted EMS response time. Response time was calculated as the minimum of the drone and EMS response times. The performance of our dispatch rule was compared on out-of-sample OHCAs using 5-fold cross validation to the baseline cases of (1) no drones, and (2) drone dispatch to every suspected OHCA. Statistical analysis on the median response times was performed using a right-tailed sign test. Results: We identified 4774 suspected OHCAs with a median historical EMS response time of 6.0 minutes. Using our dispatch rule, median response time was significantly shorter at 3.9 minutes (P<0.001). Drones were dispatched to 3803 cases (79.7%) and of those, drone response was faster than EMS in 3076 cases (80.9%). When the drone was not dispatched, it would have been slower than EMS in 856 cases (88.1%). Sending a drone to every suspected OHCA resulted in an identical median response time of 3.9 minutes (P<0.001), with drones arriving before EMS in 3191 cases (66.8%). Conclusion: A machine learning-based dispatch rule can achieve similar response times as a policy that dispatches a drone to all suspected OHCAs, while dispatching drones less frequently.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".