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This is a Pre-Registered Trial study.

December 13, 2025BMC Emergency MedicineOpen Access

Identifying optimal locations for automated external defibrillators (AED) in Freiburg: development and validation of a machine learning model based on demographic and infrastructural data

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Predictive modelling improves AED coverage for out-of-hospital cardiac arrest in urban settings, indicating enhanced emergency response strategies.

This research aims to identify optimal locations for automated external defibrillators (AED) using a machine learning model.
Analyzed 5,076 alerts of suspected OHCA and 95 AED locations in Freiburg from 2018 to 2024.
Developed a machine learning model based on demographic and infrastructural data.
Used a Decision Tree and greedy algorithm to determine AED placements.
Increasing AED placement coverage from 21.6% to 42.4% without additional devices.
ML model coverage was only 6.7% lower than historical alert data coverage.
A 20% increase in AEDs raised coverage to 30.5%.

Ganter et al. (2025) studied this question.

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Predictive modelling improves AED coverage for out-of-hospital cardiac arrest in urban settings, indicating enhanced emergency response strategies.