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Clinical support system for triage based on federated learning for the Korea triage and acuity scale

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dc.contributor.author유재용-
dc.date.accessioned2024-05-30T06:56:12Z-
dc.date.available2024-05-30T06:56:12Z-
dc.date.issued2023-08-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/199456-
dc.description.abstractBackground and aims: This study developed a clinical support system based on federated learning to predict the need for a revised Korea Triage Acuity Scale (KTAS) to facilitate triage.Methods: This was a retrospective study that used data from 11,952,887 patients in the Korean National Emergency Department Information System (NEDIS) from 2016 to 2018 for model development. Separate cohorts were created based on the emergency medical center level in the NEDIS: regional emergency medical center (REMC), local emergency medical center (LEMC), and local emergency medical institution (LEMI). External and temporal validation used data from emergency department (ED) of the study site from 2019 to 2021. Patient features obtained during the triage process and the initial KTAS scores were used to develop the prediction model. Federated learning was used to rectify the disparity in data quality between EDs. The patient's demographic information, vital signs in triage, mental status, arrival information, and initial KTAS were included in the input feature.Results: 3,626,154 patients' visits were included in the regional emergency medical center cohort; 8,278,081 patients' visits were included in the local emergency medical center cohort; and 48,652 patients' visits were included in the local emergency medical institution cohort. The study site cohort, which is used for external and temporal validation, included 135,780 patients visits. Among the patients in the REMC and study site cohorts, KTAS level 3 patients accounted for the highest proportion at 42.4% and 45.1%, respectively, whereas in the LEMC and LEMI cohorts, KTAS level 4 patients accounted for the highest proportion. The area under the receiver operating characteristic curve for the prediction model was 0.786, 0.750, and 0.770 in the external and temporal validation. Patients with revised KTAS scores had a higher admission rate and ED mortality rate than those with unaltered KTAS scores.-
dc.description.statementOfResponsibilityopen-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfHELIYON-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titleClinical support system for triage based on federated learning for the Korea triage and acuity scale-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Biomedical Systems Informatics (의생명시스템정보학교실)-
dc.contributor.googleauthorHansol Chang-
dc.contributor.googleauthorJae Yong Yu-
dc.contributor.googleauthorGeun Hyeong Lee-
dc.contributor.googleauthorSejin Heo-
dc.contributor.googleauthorSe Uk Lee-
dc.contributor.googleauthorSung Yeon Hwang-
dc.contributor.googleauthorHee Yoon-
dc.contributor.googleauthorWon Chul Cha-
dc.contributor.googleauthorTae Gun Shin-
dc.contributor.googleauthorMin Seob Sim-
dc.contributor.googleauthorIk Joon Jo-
dc.contributor.googleauthorTaerim Kim-
dc.identifier.doi10.1016/j.heliyon.2023.e19210-
dc.contributor.localIdA06594-
dc.relation.journalcodeJ04313-
dc.identifier.eissn2405-8440-
dc.identifier.pmid37654468-
dc.subject.keywordClinical decision-making-
dc.subject.keywordEmergency department-
dc.subject.keywordEmergency medical service-
dc.subject.keywordMachine learning-
dc.subject.keywordTriage-
dc.contributor.alternativeNameYu, Jae Yong-
dc.contributor.affiliatedAuthor유재용-
dc.citation.volume9-
dc.citation.number8-
dc.citation.startPagee19210-
dc.identifier.bibliographicCitationHELIYON, Vol.9(8) : e19210, 2023-08-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers

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