• Journal of the Society of Disaster Information
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• v.19 no.1
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• pp.44-59
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• 2023

Purpose: In the event of mass casualties, triage must be done promptly and accurately so that as many patients as possible can be recovered and returned to the battlefield. However, medical personnel have received many tasks with less manpower, and the battlefield for classifying patients is too complex and uncertain. Therefore, we studied an artificial intelligence model that can assist and replace medical personnel on the battlefield. Method: The triage model is presented using reinforcement learning, a field of artificial intelligence. The learning of the model is conducted to find a policy that allows as many patients as possible to be treated, taking into account the condition of randomly set patients and the medical capability of the military hospital. Result: Whether the reinforcement learning model progressed well was confirmed through statistical graphs such as cumulative reward values. In addition, it was confirmed through the number of survivors whether the triage of the learned model was accurate. As a result of comparing the performance with the rule-based model, the reinforcement learning model was able to rescue 10% more patients than the rule-based model. Conclusion: Through this study, it was found that the triage model using reinforcement learning can be used as an alternative to assisting and replacing triage decision-making of medical personnel in the case of mass casualties.

• The Korean Journal of Emergency Medical Services
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• v.26 no.2
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• pp.73-85
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• 2022

Purpose: The purpose of this study is to understand currently active Korean paramedics' disaster response abilities, including immediate response, severity classification, patient treatment, and patient transfer, in a disaster situation with multiple casualties. Methods: A structured questionnaire consisting of a total of 25 questions was used, including 5 questions on the subject's general characteristics and 20 questions on disaster-related emergency response abilities. Results: Among the disaster response abilities of the participants, the patient transport ability scores were high and the cooperative support ability scores were low. In terms of general characteristics, there was a significant difference in age, and it was high in the 40s, and there was a significant positive correlation between each competency. Conclusion: These results suggest that there is an urgent need to develop a systematic and specialized educational system with components inside and outside fire departments related to multiple casualty disasters to improve overall abilities.