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Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time

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dc.contributor.author김다영-
dc.contributor.author동재준-
dc.contributor.author송영구-
dc.contributor.author이경화-
dc.contributor.author현종훈-
dc.contributor.author김수빈-
dc.date.accessioned2022-03-11T05:57:03Z-
dc.date.available2022-03-11T05:57:03Z-
dc.date.issued2022-01-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/187877-
dc.description.abstractEarly detection of bacteremia is important to prevent antibiotic abuse. Therefore, we aimed to develop a clinically applicable bacteremia prediction model using machine learning technology. Data from two tertiary medical centers' electronic medical records during a 12-year-period were extracted. Multi-layer perceptron (MLP), random forest, and gradient boosting algorithms were applied for machine learning analysis. Clinical data within 12 and 24 hours of blood culture were analyzed and compared. Out of 622,771 blood cultures, 38,752 episodes of bacteremia were identified. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUROC) of the prediction performance in 12- and 24-h data models was 0.762 (95% confidence interval (CI); 0.7617-0.7623) and 0.753 (95% CI; 0.7520-0.7529), respectively. AUROC of causative-pathogen subgroup analysis predictive value for Acinetobacter baumannii bacteremia was the highest at 0.839 (95% CI; 0.8388-0.8394). Compared to primary bacteremia, AUROC of sepsis caused by pneumonia was highest. Predictive performance of bacteremia was superior in younger age groups. Bacteremia prediction using machine learning technology appeared possible for acute infectious diseases. This model was more suitable especially to pneumonia caused by Acinetobacter baumannii. From the 24-h blood culture data, bacteremia was predictable by substituting only the continuously variable values.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherMDPI AG-
dc.relation.isPartOfDIAGNOSTICS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titlePrediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorKyoung Hwa Lee-
dc.contributor.googleauthorJae June Dong-
dc.contributor.googleauthorSubin Kim-
dc.contributor.googleauthorDayeong Kim-
dc.contributor.googleauthorJong Hoon Hyun-
dc.contributor.googleauthorMyeong-Hun Chae-
dc.contributor.googleauthorByeong Soo Lee-
dc.contributor.googleauthorYoung Goo Song-
dc.identifier.doi10.3390/diagnostics12010102-
dc.contributor.localIdA06226-
dc.contributor.localIdA04927-
dc.contributor.localIdA02037-
dc.contributor.localIdA04620-
dc.contributor.localIdA05944-
dc.relation.journalcodeJ03798-
dc.identifier.eissn2075-4418-
dc.identifier.pmid35054269-
dc.subject.keywordbacteremia-
dc.subject.keyworddata extraction time-
dc.subject.keywordmachine learning-
dc.subject.keywordprediction-
dc.contributor.alternativeNameKim, Dayeong-
dc.contributor.affiliatedAuthor김다영-
dc.contributor.affiliatedAuthor동재준-
dc.contributor.affiliatedAuthor송영구-
dc.contributor.affiliatedAuthor이경화-
dc.contributor.affiliatedAuthor현종훈-
dc.citation.volume12-
dc.citation.number1-
dc.citation.startPage102-
dc.identifier.bibliographicCitationDIAGNOSTICS, Vol.12(1) : 102, 2022-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Family Medicine (가정의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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