Cited 9 times in
Long-term PM 2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation
DC Field | Value | Language |
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dc.contributor.author | 김인수 | - |
dc.contributor.author | 김종윤 | - |
dc.contributor.author | 김태훈 | - |
dc.contributor.author | 박희남 | - |
dc.contributor.author | 엄재선 | - |
dc.contributor.author | 유희태 | - |
dc.contributor.author | 이문형 | - |
dc.contributor.author | 정보영 | - |
dc.date.accessioned | 2020-12-01T16:58:33Z | - |
dc.date.available | 2020-12-01T16:58:33Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/180070 | - |
dc.description.abstract | Clinical impact of fine particulate matter (PM2.5) air pollution on incident atrial fibrillation (AF) had not been well studied. We used integrated machine learning (ML) to build several incident AF prediction models that include average hourly measurements of PM2.5 for the 432,587 subjects of Korean general population. We compared these incident AF prediction models using c-index, net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI). ML using the boosted ensemble method exhibited a higher c-index (0.845 [0.837-0.853]) than existing traditional regression models using CHA2DS2-VASc (0.654 [0.646-0.661]), CHADS2 (0.652 [0.646-0.657]), or HATCH (0.669 [0.661-0.676]) scores (each p < 0.001) for predicting incident AF. As feature selection algorithms identified PM2.5 as a highly important variable, we applied PM2.5 for predicting incident AF and constructed scoring systems. The prediction performances significantly increased compared with models without PM2.5 (c-indices: boosted ensemble ML, 0.954 [0.949-0.959]; PM-CHA2DS2-VASc, 0.859 [0.848-0.870]; PM-CHADS2, 0.823 [0.810-0.836]; or PM-HATCH score, 0.849 [0.837-0.860]; each interaction, p < 0.001; NRI and IDI were also positive). ML combining readily available clinical variables and PM2.5 data was found to predict incident AF better than models without PM2.5 or even established risk prediction approaches in the general population exposed to high air pollution levels. | - |
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.title | Long-term PM 2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | In-Soo Kim | - |
dc.contributor.googleauthor | Pil-Sung Yang | - |
dc.contributor.googleauthor | Eunsun Jang | - |
dc.contributor.googleauthor | Hyunjean Jung | - |
dc.contributor.googleauthor | Seng Chan You | - |
dc.contributor.googleauthor | Hee Tae Yu | - |
dc.contributor.googleauthor | Tae-Hoon Kim | - |
dc.contributor.googleauthor | Jae-Sun Uhm | - |
dc.contributor.googleauthor | Hui-Nam Pak | - |
dc.contributor.googleauthor | Moon-Hyoung Lee | - |
dc.contributor.googleauthor | Jong-Youn Kim | - |
dc.contributor.googleauthor | Boyoung Joung | - |
dc.identifier.doi | 10.1038/s41598-020-73537-8 | - |
dc.contributor.localId | A00840 | - |
dc.contributor.localId | A00926 | - |
dc.contributor.localId | A01085 | - |
dc.contributor.localId | A01776 | - |
dc.contributor.localId | A02337 | - |
dc.contributor.localId | A02535 | - |
dc.contributor.localId | A02766 | - |
dc.contributor.localId | A03609 | - |
dc.relation.journalcode | J02646 | - |
dc.identifier.eissn | 2045-2322 | - |
dc.identifier.pmid | 33004983 | - |
dc.contributor.alternativeName | Kim, In-Soo | - |
dc.contributor.affiliatedAuthor | 김인수 | - |
dc.contributor.affiliatedAuthor | 김종윤 | - |
dc.contributor.affiliatedAuthor | 김태훈 | - |
dc.contributor.affiliatedAuthor | 박희남 | - |
dc.contributor.affiliatedAuthor | 엄재선 | - |
dc.contributor.affiliatedAuthor | 유희태 | - |
dc.contributor.affiliatedAuthor | 이문형 | - |
dc.contributor.affiliatedAuthor | 정보영 | - |
dc.citation.volume | 10 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 16324 | - |
dc.identifier.bibliographicCitation | SCIENTIFIC REPORTS, Vol.10(1) : 16324, 2020-10 | - |
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