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

ABSTRACT:

Introduction

Out-of-hospital cardiac arrest (OHCA) is a critical medical emergency where rapid access to automated external defibrillators (AED) can significantly improve survival rates. However, there is currently a lack of well-established frameworks and guidelines concerning the optimal placement of AED. Additionally, historical data on the locations of OHCA incidents is often unavailable or incomplete. This study seeks to address these gaps by analyzing the most effective AED placement strategies and evaluating the impact of additional AED locations on suspected OHCA cases. To achieve this, a machine learning (ML) model is developed that relies exclusively on demographic and infrastructural factors, without the need for historical OHCA location data.

Methods

In this data

SUBMITTER: Ganter J 

PROVIDER: S-EPMC12817458 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature