Cited 13 times in
A new statistical approach to predict bacteremia using electronic medical records
DC Field | Value | Language |
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dc.contributor.author | 송영구 | - |
dc.contributor.author | 윤지현 | - |
dc.contributor.author | 진성준 | - |
dc.date.accessioned | 2014-12-18T09:10:17Z | - |
dc.date.available | 2014-12-18T09:10:17Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 0036-5548 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/87666 | - |
dc.description.abstract | Background: Previous attempts to predict bacteremia have focused on selecting significant variables. However, these approaches have had limitations such as poor reproducibility in prediction accuracy and inconsistency in predictor selection. Here we propose a Bayesian approach to predict bacteremia based on the statistical distributions of clinical variables of previous patients, which has recently become possible through the adoption of electronic medical records. Methods: In a derivation cohort, Bayesian prediction models were derived and their discriminative performance was compared with previous models under varying combinations of predictors. Then the Bayesian models were prospectively tested in a validation cohort. According to Bayesian probabilities of bacteremia, patients in both cohorts were grouped into bacteremia risk groups. Results: Using the same prediction variables, the Bayesian predictions were more accurate than conventional rule-based predictions. Moreover, their better discriminative performance remained consistent despite variations in clinical variables. The receiver operating characteristic (ROC) area of the Bayesian model with 20 predictors was 0.70 ± 0.007 in the derivation cohort and 0.70 ± 0.018 in the validation cohort. The prevalence of bacteremia in groups I, II, and VI (grouped according to probability ratio) were 1.9%, 3.4%, and 20.0% in the derivation cohort, and 0.4%, 3.2%, and 18.4% in the validation cohort, respectively. The overall prevalence of bacteremia was 6.9% in both cohorts. Conclusions: In the present study, the Bayesian prediction model showed stable performance in predicting bacteremia and identifying risk groups, as the previous models did. The clinical significance of the Bayesian approach is expected to be demonstrated through a multicenter trial. | - |
dc.description.statementOfResponsibility | open | - |
dc.relation.isPartOf | SCANDINAVIAN JOURNAL OF INFECTIOUS DISEASES | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Bacteremia/diagnosis* | - |
dc.subject.MESH | Bacteremia/epidemiology* | - |
dc.subject.MESH | Bayes Theorem | - |
dc.subject.MESH | Electronic Health Records* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Models, Statistical* | - |
dc.subject.MESH | Prospective Studies | - |
dc.subject.MESH | ROC Curve | - |
dc.subject.MESH | Reproducibility of Results | - |
dc.subject.MESH | Retrospective Studies | - |
dc.subject.MESH | Risk Factors | - |
dc.title | A new statistical approach to predict bacteremia using electronic medical records | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학) | - |
dc.contributor.googleauthor | Sung Joon Jin | - |
dc.contributor.googleauthor | Mingoo Kim | - |
dc.contributor.googleauthor | Ji Hyun Yoon | - |
dc.contributor.googleauthor | Young Goo Song | - |
dc.identifier.doi | 10.3109/00365548.2013.799287 | - |
dc.admin.author | false | - |
dc.admin.mapping | false | - |
dc.contributor.localId | A02037 | - |
dc.contributor.localId | A02609 | - |
dc.contributor.localId | A03983 | - |
dc.relation.journalcode | J02634 | - |
dc.identifier.eissn | 1651-1980 | - |
dc.identifier.pmid | 23808716 | - |
dc.identifier.url | http://informahealthcare.com/doi/abs/10.3109/00365548.2013.799287 | - |
dc.subject.keyword | Bacteremia | - |
dc.subject.keyword | clinical prediction rule | - |
dc.subject.keyword | Bayesian prediction | - |
dc.subject.keyword | electronic medical records | - |
dc.contributor.alternativeName | Song, Young Goo | - |
dc.contributor.alternativeName | Yoon, Ji Hyun | - |
dc.contributor.alternativeName | Jin, Sung Joon | - |
dc.contributor.affiliatedAuthor | Song, Young Goo | - |
dc.contributor.affiliatedAuthor | Yoon, Ji Hyun | - |
dc.contributor.affiliatedAuthor | Jin, Sung Joon | - |
dc.rights.accessRights | not free | - |
dc.citation.volume | 45 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 672 | - |
dc.citation.endPage | 680 | - |
dc.identifier.bibliographicCitation | SCANDINAVIAN JOURNAL OF INFECTIOUS DISEASES, Vol.45(9) : 672-680, 2013 | - |
dc.identifier.rimsid | 32194 | - |
dc.type.rims | ART | - |
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