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739. Unlocking the Value of Extensive Data: Estimating spatial cardiac arrest risk to guide resource allocation decisions
Invited abstract in session TD-31: Analytics for Combinatorial Problems from Health Care to the Food Industry, stream Analytics.
Tuesday, 14:30-16:00Room: 046 (building: 208)
Authors (first author is the speaker)
1. | Derya Demirtas
|
Industrial Engineering & Business Information Systems, University of Twente | |
2. | Robin Buter
|
Industrial Engineering & Business Information Systems, University of Twente | |
3. | Remy Stieglis
|
Amsterdam UMC | |
4. | Hans van Schuppen
|
Amsterdam UMC |
Abstract
Out-of-hospital cardiac arrest (OHCA) is a significant public health problem with notably low survival rates. Early defibrillation is crucial for survival, highlighting the importance of nearby automated external defibrillators (AEDs). Current AED placement strategies often rely on historical OHCA data, which are limited in availability. Publicly available demographic/socioeconomic data are often easily available and shown to have correlations with OHCA risk. This study aims to 1) estimate spatial cardiac arrest risk using demographic/socioeconomic data alone 2) compare AED location models based solely on estimated risk with those incorporating historical OHCA data to inform demand. Machine learning techniques were applied to a comprehensive dataset spanning multiple municipalities. Predicted OHCA incidence of each district were used to optimize AED locations, alongside AED optimization models that used smoothed out historical cardiac arrest data as demand. Results on several municipalities underscore the value of an OHCA registry. Nonetheless, in its absence, machine learning models leveraging demographic and socioeconomic data offer a viable means to substantially enhance coverage.
Keywords
- Health Care
- Machine Learning
- Location
Status: accepted
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