Matches in SemOpenAlex for { <https://semopenalex.org/work/W2066510964> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W2066510964 endingPage "680" @default.
- W2066510964 startingPage "672" @default.
- W2066510964 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." @default.
- W2066510964 created "2016-06-24" @default.
- W2066510964 creator A5005105394 @default.
- W2066510964 creator A5023827380 @default.
- W2066510964 creator A5045202191 @default.
- W2066510964 creator A5082053473 @default.
- W2066510964 date "2013-07-01" @default.
- W2066510964 modified "2023-10-16" @default.
- W2066510964 title "A new statistical approach to predict bacteremia using electronic medical records" @default.
- W2066510964 cites W1980868990 @default.
- W2066510964 cites W1982482813 @default.
- W2066510964 cites W2030346622 @default.
- W2066510964 cites W2043687334 @default.
- W2066510964 cites W2125365733 @default.
- W2066510964 cites W2149607859 @default.
- W2066510964 cites W2160568168 @default.
- W2066510964 cites W2166740672 @default.
- W2066510964 cites W2322344843 @default.
- W2066510964 cites W4319293013 @default.
- W2066510964 doi "https://doi.org/10.3109/00365548.2013.799287" @default.
- W2066510964 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/23808716" @default.
- W2066510964 hasPublicationYear "2013" @default.
- W2066510964 type Work @default.
- W2066510964 sameAs 2066510964 @default.
- W2066510964 citedByCount "15" @default.
- W2066510964 countsByYear W20665109642014 @default.
- W2066510964 countsByYear W20665109642015 @default.
- W2066510964 countsByYear W20665109642016 @default.
- W2066510964 countsByYear W20665109642018 @default.
- W2066510964 countsByYear W20665109642019 @default.
- W2066510964 countsByYear W20665109642020 @default.
- W2066510964 countsByYear W20665109642021 @default.
- W2066510964 countsByYear W20665109642022 @default.
- W2066510964 countsByYear W20665109642023 @default.
- W2066510964 crossrefType "journal-article" @default.
- W2066510964 hasAuthorship W2066510964A5005105394 @default.
- W2066510964 hasAuthorship W2066510964A5023827380 @default.
- W2066510964 hasAuthorship W2066510964A5045202191 @default.
- W2066510964 hasAuthorship W2066510964A5082053473 @default.
- W2066510964 hasConcept C105795698 @default.
- W2066510964 hasConcept C107673813 @default.
- W2066510964 hasConcept C126322002 @default.
- W2066510964 hasConcept C154945302 @default.
- W2066510964 hasConcept C201903717 @default.
- W2066510964 hasConcept C207201462 @default.
- W2066510964 hasConcept C2779443120 @default.
- W2066510964 hasConcept C33923547 @default.
- W2066510964 hasConcept C41008148 @default.
- W2066510964 hasConcept C501593827 @default.
- W2066510964 hasConcept C58471807 @default.
- W2066510964 hasConcept C71924100 @default.
- W2066510964 hasConcept C72563966 @default.
- W2066510964 hasConcept C86803240 @default.
- W2066510964 hasConcept C89423630 @default.
- W2066510964 hasConcept C97931131 @default.
- W2066510964 hasConceptScore W2066510964C105795698 @default.
- W2066510964 hasConceptScore W2066510964C107673813 @default.
- W2066510964 hasConceptScore W2066510964C126322002 @default.
- W2066510964 hasConceptScore W2066510964C154945302 @default.
- W2066510964 hasConceptScore W2066510964C201903717 @default.
- W2066510964 hasConceptScore W2066510964C207201462 @default.
- W2066510964 hasConceptScore W2066510964C2779443120 @default.
- W2066510964 hasConceptScore W2066510964C33923547 @default.
- W2066510964 hasConceptScore W2066510964C41008148 @default.
- W2066510964 hasConceptScore W2066510964C501593827 @default.
- W2066510964 hasConceptScore W2066510964C58471807 @default.
- W2066510964 hasConceptScore W2066510964C71924100 @default.
- W2066510964 hasConceptScore W2066510964C72563966 @default.
- W2066510964 hasConceptScore W2066510964C86803240 @default.
- W2066510964 hasConceptScore W2066510964C89423630 @default.
- W2066510964 hasConceptScore W2066510964C97931131 @default.
- W2066510964 hasIssue "9" @default.
- W2066510964 hasLocation W20665109641 @default.
- W2066510964 hasLocation W20665109642 @default.
- W2066510964 hasOpenAccess W2066510964 @default.
- W2066510964 hasPrimaryLocation W20665109641 @default.
- W2066510964 hasRelatedWork W2013551692 @default.
- W2066510964 hasRelatedWork W2149607859 @default.
- W2066510964 hasRelatedWork W2507458234 @default.
- W2066510964 hasRelatedWork W2603773853 @default.
- W2066510964 hasRelatedWork W2748952813 @default.
- W2066510964 hasRelatedWork W2752271443 @default.
- W2066510964 hasRelatedWork W2899084033 @default.
- W2066510964 hasRelatedWork W4220997548 @default.
- W2066510964 hasRelatedWork W4310500762 @default.
- W2066510964 hasRelatedWork W4385525161 @default.
- W2066510964 hasVolume "45" @default.
- W2066510964 isParatext "false" @default.
- W2066510964 isRetracted "false" @default.
- W2066510964 magId "2066510964" @default.
- W2066510964 workType "article" @default.