Public Access Defibrillator Deployment for Cardiac Arrests: A Learn-Then-Optimize Approach with SHAP-based Interpretable Analytics

TL;DR

This paper presents a novel approach combining machine learning, SHAP interpretability, and integer programming to optimize AED deployment for out-of-hospital cardiac arrests, aiming to improve survival rates through better prediction and placement strategies.

Contribution

It introduces a learn-then-optimize framework that uses geographic data and SHAP analytics to guide AED deployment, enhancing interpretability and practical effectiveness.

Findings

High predictive accuracy of the machine learning model.

SHAP analytics reveal key geographic features influencing OHCA.

Optimized AED deployment improves coverage and response times.

Abstract

Out-of-hospital cardiac arrest (OHCA) survival rates remain extremely low due to challenges in the timely accessibility of medical devices. Therefore, effective deployment of automated external defibrillators (AED) can significantly increase survival rates. Precise and interpretable predictions of OHCA occurrences provide a solid foundation for efficient and robust AED deployment optimization. This study develops a novel learn-then-optimize approach, integrating three key components: a machine learning prediction model, SHAP-based interpretable analytics, and a SHAP-guided integer programming (SIP) model. The machine learning model is trained utilizing only geographic data as inputs to overcome data availability obstacles, and its strong predictive performance validates the feasibility of interpretation. Furthermore, the SHAP model elaborates on the contribution of each geographic feature to the OHCA occurrences. Finally, an integer programming model is formulated for optimizing AED deployment, incorporating SHAP-weighted OHCA densities. Various numerical experiments are conducted across different settings. Based on comparative and sensitive analysis, the optimization effect of our approach is verified and valuable insights are derived to provide substantial support for theoretical extension and practical implementation.