Using Artificial Intelligence for Predicting the Duration of Emergency Evacuation During Hospital Fire. (10th October 2023)

• Record Type: Journal Article
• Authors: Sahebi, Ali; Jahangiri, Katayoun; Alibabaei, Ahmad; Khorasani-Zavareh, Davoud
• Is Part Of: Disaster medicine and public health preparedness. Volume 17(2023)
• Publication Date: 2023-10-10
• Subjects: hospital -- fire -- emergency evacuation -- artificial intelligence -- machine learning; Disaster medicine -- Periodicals; Emergency management -- Planning -- Periodicals; Public health -- Periodicals; 363.34
• DOI: 10.1017/dmp.2022.187
• Languages: English
• ISSNs: 1935-7893

Abstract

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.