Cited 0 times in
Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김민정 | - |
dc.contributor.author | 김현창 | - |
dc.contributor.author | 장혁재 | - |
dc.contributor.author | 김지훈 | - |
dc.date.accessioned | 2023-05-31T05:38:14Z | - |
dc.date.available | 2023-05-31T05:38:14Z | - |
dc.date.issued | 2023-04 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/194236 | - |
dc.description.abstract | BACKGROUND: This study aimed to develop a prediction model for transferring patients to an inappropriate hospital for suspected cardiovascular emergency diseases at the pre-hospital stage, using variables obtained from an integrated nationwide dataset, and to assess the performance of this model. METHODS: We integrated three nationwide datasets and developed a two-step prediction model utilizing a machine learning algorithm. Ninety-eight clinical characteristics of patients identified at the pre-hospital stage and 13 hospital components were used as input data for the model. The primary endpoint of the model was the prediction of transfer to an inappropriate hospital. RESULTS: A total of 94,256 transferred patients in the public pre-hospital care system matched the National Emergency Department Information System data of patients with a pre-hospital cardiovascular registry created in South Korea between July 2017 and December 2018. Of these, 1,770 (6.26%) patients failed to be transferred to a capable hospital. The area under the receiver operating characteristic curve of the final predictive model was 0.813 (0.800-0.825), and the area under the receiver precision-recall curve was 0.286 (0.265-0.308). CONCLUSIONS: Our prediction model used machine learning to show favorable performance in transferring patients with suspected cardiovascular disease to a capable hospital. For our results to lead to changes in the pre-hospital care system, a digital platform for sharing real-time information should be developed. © 2023. The Author(s). | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | BioMed Central | - |
dc.relation.isPartOf | BMC MEDICAL INFORMATICS AND DECISION MAKING | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Cardiovascular Diseases* / diagnosis | - |
dc.subject.MESH | Cardiovascular Diseases* / therapy | - |
dc.subject.MESH | Emergency Service, Hospital | - |
dc.subject.MESH | Hospitals | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | ROC Curve | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Emergency Medicine (응급의학교실) | - |
dc.contributor.googleauthor | Ji Hoon Kim | - |
dc.contributor.googleauthor | Bomgyeol Kim | - |
dc.contributor.googleauthor | Min Joung Kim | - |
dc.contributor.googleauthor | Heejung Hyun | - |
dc.contributor.googleauthor | Hyeon Chang Kim | - |
dc.contributor.googleauthor | Hyuk-Jae Chang | - |
dc.identifier.doi | 10.1186/s12911-023-02149-9 | - |
dc.contributor.localId | A00470 | - |
dc.contributor.localId | A01142 | - |
dc.contributor.localId | A03490 | - |
dc.relation.journalcode | J00363 | - |
dc.identifier.eissn | 1472-6947 | - |
dc.identifier.pmid | 37024872 | - |
dc.subject.keyword | Cardiovascular emergency disease | - |
dc.subject.keyword | Inappropriate hospital | - |
dc.subject.keyword | Machine learning | - |
dc.subject.keyword | Pre-hospital transfer | - |
dc.contributor.alternativeName | Kim, Min Joung | - |
dc.contributor.affiliatedAuthor | 김민정 | - |
dc.contributor.affiliatedAuthor | 김현창 | - |
dc.contributor.affiliatedAuthor | 장혁재 | - |
dc.citation.volume | 23 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 56 | - |
dc.identifier.bibliographicCitation | BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol.23(1) : 56, 2023-04 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.