iRescU - Data for Social Good Saving Lives Bridging the Gaps in Sudden Cardiac Arrest Survival

TL;DR

The paper presents iRescU, a system that enhances AED accessibility and placement through crowdsourcing, geolocation, and machine learning, aiming to improve survival rates from sudden cardiac arrest in communities.

Contribution

It introduces a comprehensive AED database augmented by crowdsourcing and machine learning to optimize AED placement and accessibility for SCA emergencies.

Findings

Increased AED accessibility through crowdsourcing.

Identification of high-need areas for AED deployment.

Potential to significantly improve SCA survival rates.

Abstract

Currently every day in the USA 1000 people die of sudden cardiac arrest (SCA) outside of hospitals or ambulances - before emergency medical help arrives - in the streets, workplaces, schools and homes of our cities, adults and children. Brain death commences in 3 minutes, and often the ambulance just can't be there in time. Citizen cardiopulmonary resuscitation (CPR) and automated external defibrillator (AED) use can save precious minutes and lives. Using public access AED's saves lives in SCA- however AEDs are used in <2% of cardiac arrests, though could save lives in 80% if available, findable, functioning, and used. The systems problem to solve is that there is no comprehensive or real time accessible database of the AED locations, and also it is not known that they are actually being positioned where they are needed. The iRescU project is designed to bridge this gap in SCA survival, by substantially augmenting the AED database. Utilizing a combination of AED crowd sourcing and geolocation integrated with existing 911 services and SCA events and projected events based on machine learning data information to help make the nearest AED accessible and available in the setting of a SCA emergency and to identify the areas of greatest need for AEDs to be positioned in the community. Helping to save lives and address preventable death with a social good approach and applied big data.