Submitting Campus

Daytona Beach

Department

Human Factors and Behavioral Neurobiology

Document Type

Article

Publication/Presentation Date

2025

Abstract/Description

INTRODUCTION: T his study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By investigating predictors of fatalities after helicopter emergency medical service crashes through the application of machine learning techniques, we updated existing data sets and sought to uncover patterns that traditional analysis might not reveal.

METHODS: Using the National Transportation Safety Board database, the authors analyzed a dataset of 267 helicopter emergency medical service accidents between 1991–2022. We first calculated fatalities odds ratios for each condition. We then plotted geospatial locations of all reported accidents. Finally, we used XGBoost regression to understand the most important features contributing to fatality after an accident.

RESULTS: T he findings reaffirm previous research and identify significant predictors of fatalities in helicopter emergency medical service accidents. Key factors such as adverse flight conditions (weather), the absence of a copilot, and postcrash fires are highlighted as critical to understanding and mitigating risks of fatality.

DISCUSSION: T hese findings emphasize the utility of machine learning in extracting meaningful insights from accident data, suggesting that such techniques offer a more nuanced understanding of the conditions leading to fatalities. It points out the potential of these methods to not only enhance aviation safety but also to be applied across other sectors. We conclude by underlining the significant potential of techniques like XGBoost in advancing safety measures within helicopter emergency medical service and possibly other aviation sectors.

Publication Title

Aerospace Medicine and Human Performance

DOI

https://doi.org/10.3357/AMHP.6461.2025

Publisher

Aerospace Medical Association

Download

Share

COinS