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- W2260707391 abstract "Ventilator weaning is the process of discontinuing mechanical ventilators from patients with respiratory failure. This study designed a clinical decision support system (CDSS) using support vector machine (SVM) to predict if a patient can be weaned from mechanical ventilator successfully. Alter method based on logistic regression anal- ysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were adopted to select salient features in 27 variables, including demographic information, physiology and disease factors, and care and treatment factors for CDSS. Data of 348 patients were collected at four different periods from all-purpose respiratory care cen- ter. Seven signicant variables ( p < 0 :05 ) using LRA contrasted to eleven variables using RFE algorithm were selected. The predictive accuracy under cross-validation is 88.33% (LRA) and 92.73% (RFE), respectively. The systems outperform predictors (75- 78%) built using frequency-to-tid al volume ratio (" @default.
- W2260707391 created "2016-06-24" @default.
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- W2260707391 date "2012-01-01" @default.
- W2260707391 modified "2023-09-22" @default.
- W2260707391 title "DESIGN OF A CLINICAL DECISION SUPPORT FOR DETERMINING VENTILATOR WEANING USING SUPPORT VECTOR MACHINE" @default.
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