Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8620345 | Journal of Critical Care | 2018 | 10 Pages |
Abstract
We first reported HAVM incidence in a neuro-ICU in Russia. We showed that tree-based ML is an effective approach to study risk factors because it enables the identification of nonlinear interaction across factors. We suggest that the number of found risk factors and the duration of their presence in patients should be reduced to prevent HAVM.
Keywords
XGBoostINSDCDCHAISSSIEETsCCINSIEVDPCABacterialICUintensive care unitPrincipal component analysisexternal ventricular drainEndoscopic endonasal transsphenoidal surgeryRelative riskCharlson Comorbidity IndexRandom Forest classifiercross infectionhealthcare-associated infectionRisk factorsconfidence intervalCSFCerebrospinal fluidarea under the receiver-operating characteristic curveMeningitisodds ratioIntracranial pressure monitoringInfection controlMachine learning
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Authors
Ivan Savin, Ksenia Ershova, Nataliya Kurdyumova, Olga Ershova, Oleg Khomenko, Gleb Danilov, Michael Shifrin, Vladimir Zelman,