Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

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

Sun Apr 21 00:38:22 2024

(UNSPECIFIED) Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire. Disaster Medicine and Public Health Preparedness. p. 5. ISSN 1935-7893

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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.

Item Type: Article
Creators:
CreatorsEmail
Sahebi, A.UNSPECIFIED
Jahangiri, K.UNSPECIFIED
Alibabaei, A.UNSPECIFIED
Khorasani-Zavareh, D.UNSPECIFIED
Keywords: hospital fire emergency evacuation artificial intelligence machine learning time-series Public, Environmental & Occupational Health
Divisions:
Page Range: p. 5
Journal or Publication Title: Disaster Medicine and Public Health Preparedness
Journal Index: ISI
Identification Number: https://doi.org/10.1017/dmp.2022.187
ISSN: 1935-7893
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4036

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