University of Twente Student Theses
Estimating spatial cardiac arrest risk
Smellink, E.M.L. (2024) Estimating spatial cardiac arrest risk.
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| Abstract: | Out-of-hospital cardiac arrest (OHCA) is a significant public health problem, characterized by 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 and the predicted OHCA incidence in each district was used to optimize AED locations, alongside AED optimization models that used smoothed out historical cardiac arrest data as demand. Results were showcased using five municipalities as a test case, including Amsterdam, where the existing AED coverage was 43%. AED optimization based on the tuned model increased coverage to 55%, while historical OHCA-based models achieved 60% coverage. This 5% disparity underscores 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. |
| Item Type: | Essay (Master) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 50 technical science in general |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/100580 |
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