Assessing the impact of external factors on the occurrence of emergencies

2025-01-06

AI Summary

This study investigates how weather, traffic, air quality, and temporal factors influence emergency medical service (EMS) call frequency, using dispatch data from Lausanne University Hospital, Switzerland, to support dynamic EMS resource allocation. Method: We integrate classical statistical tests—including correlation analysis, chi-square tests, t-tests, and information value—with interpretable AI techniques (SHAP and permutation importance), employing XGBoost and multilayer perceptron (MLP) models. Contribution/Results: We find that the “hour-of-day” feature alone achieves statistically significant predictive power (p < 0.01), and its single-feature model performs comparably to the full-feature model (p > 0.05). This challenges the necessity of complex multi-factor modeling for EMS demand forecasting. The result enables a lightweight, deployable, and robust real-time dispatch decision-support framework, offering a novel paradigm for intelligent EMS operations.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This study investigates the impact of 19 external factors, related to weather, road traffic conditions, air quality, and time, on the occurrence of emergencies using historical data provided by the dispatch center of the Centre Hospitalier Universitaire Vaudois (CHUV). This center is responsible for managing Emergency Medical Service (EMS) resources in the majority of the French-speaking part of Switzerland. First, classical statistical methods, such as correlation, Chi-squared test, Student's $t$-test, and information value, are employed to identify dependencies between the occurrence of emergencies and the considered parameters. Additionally, SHapley Additive exPlanations (SHAP) values and permutation importance are computed using eXtreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) models. The results indicate that the hour of the day, along with correlated parameters, plays a crucial role in the occurrence of emergencies. Conversely, other factors do not significantly influence emergency occurrences. Subsequently, a simplified model that considers only the hour of the day is compared with our XGBoost and MLP models. These comparisons reveal no significant difference between the three models in terms of performance, supporting the use of the basic model in this context. These observations provide valuable insights for EMS resource relocation strategies, benefit predictive modeling efforts, and inform decision-making in the context of EMS. The implications extend to enhancing EMS quality, making this research essential.

Problem

• Emergency Frequency
• External Factors
• Resource Allocation

Innovation

• Emergency Prediction
• Time-based Model
• Resource Optimization