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Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire

Published online by Cambridge University Press:  10 October 2022

Ali Sahebi ,
Katayoun Jahangiri Open the ORCID record for Katayoun Jahangiri [Opens in a new window] ,
Ahmad Alibabaei  and
Davoud Khorasani-Zavareh
Ali Sahebi
Affiliation:
Non-Communicable Diseases Research Center, Ilam University of Medical Sciences, Ilam, Iran
Katayoun Jahangiri*
Affiliation:
Safty Promotion and Injury Prevention Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Health in Disasters and Emergencies, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Ahmad Alibabaei
Affiliation:
Department of E-Learning, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Davoud Khorasani-Zavareh
Affiliation:
Workplace Health Promotion Research Center, Department of Health in Emergencies and Disasters, Shahid Beheshti University of Medical Sciences, Tehran, Iran
*
Corresponding author: Katayoun Jahangiri, Email: k.jahangiri@sbmu.ac.ir.
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Abstract

Objective:

A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following hospital fire using machine-learning algorithms.

Methods:

In this study, the real emergency evacuation duration of 190 patients admitted to a hospital was predicted in a simulation based on the following 8 factors: the number of hospital floors, patient preparation and transfer time, distance to the safe location, as well as patient’s weight, age, sex, and movement capability. To design and validate the model, we used statistical models of machine learning, including Support Vector Machines Random Forest, Naive Bayes Classifier, and Artificial Neural Network.

Results:

Data analysis showed that based on the Area Under the Curve, precision, and sensitivity values of 99.5%, 92.4%, and 92.1%, respectively, the Random Forest model showed a better performance compared to other models for predicting the duration of hospital emergency evacuation during fire.

Conclusion:

Predicting evacuation duration can provide managers with accurate information and true analyses of these events. Therefore, health policy makers and managers can promote preparedness and responsiveness during fire by predicting evacuation duration and developing appropriate plans using machine learning models.

Keywords

hospitalfireemergency evacuationartificial intelligencemachine learning
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 17 , 2023 , e229
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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