Design and Clinical Verification of a Clinical Decision Support System for Determining Ventilator Weaning Based on Nonlinear Support Vector Machine

Abstract

Weaning is typically regarded as a process of discontinuing mechanical ventilation in the daily practice of an intensive care unit (ICU) Among the ICU patients about 40% need mechanical ventilator for sustaining their lives The predictive rate of successful weaning achieved only 35-60% for decisions made by physicians Clinical decision support systems (CDSSs) are promising in enhancing diagnostic performance and improving healthcare quality in clinical setting To the best of our knowledge a prospective study has never been conducted to verify the effectiveness of the CDSS in ventilator weaning before This study designed a clinical decision support system using support vector machine (SVM) to predict if a patient can be weaned from mechanical ventilator successfully A filter method based on logistic regression analysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were adopted to select salient features from 27 variables including demographic data physiology and disease factors and care and treatment factors for CDSS The CDSS capable of predicting weaning outcome and reducing duration of ventilator support for patients has been verified Data of 348 patients were collected at four different periods from an all-purpose respiratory care center Seven significant variables (p

Date of Award	2014 Jan 15
Original language	English
Supervisor	Tainsong Chen (Supervisor)