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A Novel Framework for Automated External Defibrillator Deployment (FAEDD) in Identified High Risk Residential Areas

Moran Lee, Celine (2021) A Novel Framework for Automated External Defibrillator Deployment (FAEDD) in Identified High Risk Residential Areas. Masters thesis, Dublin, National College of Ireland.

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An Automated External Defibrillator (AED) are portable devices deployed in high footfall areas that are used to resuscitate a person from Cardiac Arrest. The challenge is to identify high risk areas in need of an AED and the most optimal placement of an AED. Current research indicates this challenge is highest in residential areas which are at most risk of an Out of Hospital Cardiac Arrest (OHCA) however most public and private resources has implemented AEDs in high traffic public areas such as workplaces, train stations, airports, and shopping areas.

This research proposes a framework by applying various Bayesian CAR models using Poisson, Gaussian distributions to identify the most high-risk areas using variables related to health and material deprivation from the Irish census 2016. The AED deployment framework uses MCLP and programming to identify potential AED locations specifically targeting residential areas. The main findings from this research indicates that using various Bayesian models, when compared identifies an overlap in several areas classed as high-risk. In terms of AED deployment, the proposed MCLP model used within this paper accounts for the entire spatial area of 400 metres between potential AED locations in every direction to the boundary. This allowed for coverage of every hypothetical OHCA on every residential road.

The significance of this framework to public bodies and medical resource deployment services, is a robust, conclusive Bayesian CAR model to indicate highest risk areas and a scale for residential AED deployment based on distance and number of AEDs using MCLP. The key to application of this research is communication of resources available and if AEDs are deployed, 4 awareness of AED location and mode of transport for retrieval with the occupants of each residential unit to ensure a clear understanding of time and distance to retrieve.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Clara Chan
Date Deposited: 09 Dec 2021 14:45
Last Modified: 09 Dec 2021 14:45

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