Cited 11 times in
Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 김도균 | - |
dc.contributor.author | 정석훈 | - |
dc.contributor.author | 조재화 | - |
dc.contributor.author | 최민혁 | - |
dc.contributor.author | 최용준 | - |
dc.date.accessioned | 2022-08-23T00:35:06Z | - |
dc.date.available | 2022-08-23T00:35:06Z | - |
dc.date.issued | 2022-05 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/189509 | - |
dc.description.abstract | Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766-0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795-0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973-0.980) in hospital S and 0.955 (0.950-0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Nature Publishing Group | - |
dc.relation.isPartOf | SCIENTIFIC REPORTS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | APACHE | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Algorithms | - |
dc.subject.MESH | Electronic Health Records* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Intensive Care Units* | - |
dc.subject.MESH | Machine Learning | - |
dc.title | Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Laboratory Medicine (진단검사의학교실) | - |
dc.contributor.googleauthor | Min Hyuk Choi | - |
dc.contributor.googleauthor | Dokyun Kim | - |
dc.contributor.googleauthor | Eui Jun Choi | - |
dc.contributor.googleauthor | Yeo Jin Jung | - |
dc.contributor.googleauthor | Yong Jun Choi | - |
dc.contributor.googleauthor | Jae Hwa Cho | - |
dc.contributor.googleauthor | Seok Hoon Jeong | - |
dc.identifier.doi | 10.1038/s41598-022-11226-4 | - |
dc.contributor.localId | A04891 | - |
dc.contributor.localId | A03619 | - |
dc.contributor.localId | A05674 | - |
dc.contributor.localId | A04691 | - |
dc.contributor.localId | A06061 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 35505048 | - |
dc.contributor.alternativeName | Kim, Dokyun | - |
dc.contributor.affiliatedAuthor | 김도균 | - |
dc.contributor.affiliatedAuthor | 정석훈 | - |
dc.contributor.affiliatedAuthor | 조재화 | - |
dc.contributor.affiliatedAuthor | 최민혁 | - |
dc.contributor.affiliatedAuthor | 최용준 | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 7180 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.12(1) : 7180, 2022-05 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.