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dc.contributor.author박훈기-
dc.date.accessioned2019-12-09T19:12:00Z-
dc.date.available2019-12-09T19:12:00Z-
dc.date.issued2018-10-
dc.identifier.citationPLOS ONE, v. 13, no. 10, Article no. e0206006en_US
dc.identifier.issn1932-6203-
dc.identifier.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0206006-
dc.identifier.urihttp://repository.hanyang.ac.kr/handle/20.500.11754/120391-
dc.description.abstractIn a mass casualty incident, the factors that determine the survival rate of injured patients are diverse, but one of the key factors is the time for triage. Additionally, the main factor that determines the time of triage is the number of medical personnel. However, when relying on a small number of medical personnel, the ability to increase survivability is limited. Therefore, developing a classification model for survival prediction that can quickly and precisely triage via wearable devices without medical personnel is important. In this study, we designed a consciousness index to substitute the factor by manpower and improved the classification accuracy by applying a machine learning algorithm. First, logistic regression analysis using vital signs and a consciousness index capable of remote monitoring through wearable devices confirmed the high efficiency of the consciousness index. We then developed a classification model with high accuracy which corresponds to existing injury severity scoring systems through the machine learning algorithms. We extracted 460,865 cases which met our criteria for developing the survival prediction from the national sample project in the national trauma databank which contains 408,316 cases of blunt injury and 52,549 cases of penetrating injury. Among the dataset, 17,918 (3.9%) cases died while the other survived. The AUCs with 95% confidence intervals (Cis) for the different models with the proposed simplified consciousness score as follows: RTS (as baseline), 0.78 (95% CI = 0.775 to 0.785); logistic regression, 0.87 (95% CI = 0.862 to 0.870); random forest, 0.87 (95% CI = 0.862 to 0.872); deep neural network, 0.89 (95% CI = 0.882 to 0.890). As a result, we confirmed the possibility of remote triage using a wearable device. It is expected that the time required for triage can be effectively reduced by using the developed classification model of survival prediction.en_US
dc.description.sponsorshipThis research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP (NRF-2017M3A9E1064781). This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(NRF-2017M3A9E1064784).en_US
dc.language.isoen_USen_US
dc.publisherPUBLIC LIBRARY SCIENCEen_US
dc.subjectINJURY SEVERITY SCOREen_US
dc.subjectNEURAL-NETWORKSen_US
dc.subjectLOGISTIC-REGRESSIONen_US
dc.subjectTRAUMA SCOREen_US
dc.subjectCLASSIFICATIONen_US
dc.subjectPREDICTIONen_US
dc.subjectCAREen_US
dc.subjectCONSCIOUSNESSen_US
dc.subjectVISUALIZATIONen_US
dc.subjectALGORITHMen_US
dc.titleA data-driven artificial intelligence model for remote triage in the prehospital environmenten_US
dc.typeArticleen_US
dc.relation.no10-
dc.relation.volume13-
dc.identifier.doi10.1371/journal.pone.0206006-
dc.relation.page1-14-
dc.relation.journalPLOS ONE-
dc.contributor.googleauthorKim, Dohyun-
dc.contributor.googleauthorYou, Sungmin-
dc.contributor.googleauthorSo, Soonwon-
dc.contributor.googleauthorLee, Jongshill-
dc.contributor.googleauthorYook, Sunhyun-
dc.contributor.googleauthorJang, Dong Pyo-
dc.contributor.googleauthorKim, In Young-
dc.contributor.googleauthorPark, Eunkyoung-
dc.contributor.googleauthorCho, Kyeongwon-
dc.contributor.googleauthorCha, Won Chul-
dc.contributor.googleauthorShin, Dong Wook-
dc.contributor.googleauthorCho, Baek Hwan-
dc.contributor.googleauthorPark, Hoon-Ki-
dc.relation.code2018006288-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF MEDICINE[S]-
dc.sector.departmentDEPARTMENT OF MEDICINE-
dc.identifier.pidhoonkp-


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