Geospatial Analytics and Mathematical Optimization in Pre-Hospital Resuscitation
Bibliographic record
Abstract
Resuscitation is the process of treating critically ill patients who are at imminent risk of death; as such, treatment of resuscitation patients is time-critical and must begin as soon as possible. This thesis aims to use data analytics and mathematical optimization approaches to quantify the benefit of proposed interventions aimed at improving resuscitation response in the pre-hospital setting.The first part of this thesis considers the analysis and modelling of static resources that can be retrieved by laypersons to the patient’s location. In Chapter 2, we present a narrative-driven review of the literature surrounding public access automated external defibrillators (AEDs) as an intervention for out-of-hospital cardiac arrest (OHCA) and provide commentary on the state of public access defibrillation in Scotland. In Chapter 3, we analyze the spatial coverage and socioeconomic equity of public access AEDs for nearby OHCAs in Scotland. Compared to existing AED locations, which are neither efficiently nor equitably placed, optimization-driven AED placement can lead to effective and socioeconomically equitable coverage of OHCAs. In Chapter 4, we consider various placement strategies for publicly accessible naloxone kits for nearby opioid poisonings in Vancouver and find that an optimization-driven placement strategy leads to the most efficient coverage of opioid poisonings. The second part of this thesis considers the modelling of mobile resources which are actively dispatched to the patient’s location. In Chapter 5, we analyze the effect of varying base locations for a proposed drone-based AED delivery network for OHCAs in southern Vancouver Island and find modest differences in an otherwise substantial improvement to OHCA response times. In Chapter 6, we model the potential addition of vertical takeoff-and-landing air ambulances dedicated to OHCA response in Paris and Vancouver, and find dramatic improvements in OHCA response times, particularly at the advanced life support level. In Chapter 7, we quantify the accessibility of various strategies to deploy crews administering extracorporeal cardiopulmonary resuscitation for certain OHCA patients in Scotland and estimate the resulting increase in survival. Overall, our findings show the importance and benefit of centralized, data-driven decision making when developing systems-based interventions aimed at optimizing response to the patient.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".