Cargando…

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

BACKGROUND 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 De...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Hansol, Yu, Jae Yong, Lee, Geun Hyeong, Heo, Sejin, Lee, Se Uk, Hwang, Sung Yeon, Yoon, Hee, Cha, Won Chul, Shin, Tae Gun, Sim, Min Seob, Jo, Ik Joon, Kim, Taerim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10465866/
https://www.ncbi.nlm.nih.gov/pubmed/37654468
http://dx.doi.org/10.1016/j.heliyon.2023.e19210
_version_ 1785098759678459904
author Chang, Hansol
Yu, Jae Yong
Lee, Geun Hyeong
Heo, Sejin
Lee, Se Uk
Hwang, Sung Yeon
Yoon, Hee
Cha, Won Chul
Shin, Tae Gun
Sim, Min Seob
Jo, Ik Joon
Kim, Taerim
author_facet Chang, Hansol
Yu, Jae Yong
Lee, Geun Hyeong
Heo, Sejin
Lee, Se Uk
Hwang, Sung Yeon
Yoon, Hee
Cha, Won Chul
Shin, Tae Gun
Sim, Min Seob
Jo, Ik Joon
Kim, Taerim
author_sort Chang, Hansol
collection PubMed
description BACKGROUND 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. CONCLUSIONS: This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage.
format Online
Article
Text
id pubmed-10465866
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-104658662023-08-31 Clinical support system for triage based on federated learning for the Korea triage and acuity scale Chang, Hansol Yu, Jae Yong Lee, Geun Hyeong Heo, Sejin Lee, Se Uk Hwang, Sung Yeon Yoon, Hee Cha, Won Chul Shin, Tae Gun Sim, Min Seob Jo, Ik Joon Kim, Taerim Heliyon Research Article BACKGROUND 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. CONCLUSIONS: This novel system might accurately predict the likelihood of KTAS acuity revision and support clinician-based triage. Elsevier 2023-08-17 /pmc/articles/PMC10465866/ /pubmed/37654468 http://dx.doi.org/10.1016/j.heliyon.2023.e19210 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Chang, Hansol
Yu, Jae Yong
Lee, Geun Hyeong
Heo, Sejin
Lee, Se Uk
Hwang, Sung Yeon
Yoon, Hee
Cha, Won Chul
Shin, Tae Gun
Sim, Min Seob
Jo, Ik Joon
Kim, Taerim
Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title_full Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title_fullStr Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title_full_unstemmed Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title_short Clinical support system for triage based on federated learning for the Korea triage and acuity scale
title_sort clinical support system for triage based on federated learning for the korea triage and acuity scale
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10465866/
https://www.ncbi.nlm.nih.gov/pubmed/37654468
http://dx.doi.org/10.1016/j.heliyon.2023.e19210
work_keys_str_mv AT changhansol clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT yujaeyong clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT leegeunhyeong clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT heosejin clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT leeseuk clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT hwangsungyeon clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT yoonhee clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT chawonchul clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT shintaegun clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT simminseob clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT joikjoon clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale
AT kimtaerim clinicalsupportsystemfortriagebasedonfederatedlearningforthekoreatriageandacuityscale