An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Expert Systems with Applications: An International Journal
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Ventilator weaning is the process of discontinuing mechanical ventilation from patients with respiratory failure. Previous investigations reported that 39%-40% of the intensive care unit (ICU) patients need mechanical ventilator for sustaining their lives. Among them, 90% of the patients can be weaned from the ventilator in several days while other 5%-15% of the patients need longer ventilator support. Modern mechanical ventilators are believed to be invaluable tools for stabilizing the condition of patients in respiratory failure. However, ventilator support should be withdrawn promptly when no longer necessary so as to reduce the likelihood of known nosocomial complications and costs. Although successful ventilator weaning of ICU patients has been widely studied, indicators for accurate prediction are still under investigation. Furthermore, the predication rate of successful weaning is only 35-60% based on previous studies. It is desirable to have objective measurements and predictors of weaning that decrease the dependence on the wisdom and skill of an individual physician. However, one study showed that clinicians were often wrong when predicting weaning outcome. In this study, 189 patients, who had been supported by mechanical ventilation for longer than 21 days and were clinically stable were recruited from our all-purpose ICUs. Twenty-seven variables in total were recorded, while only 8 variables which reached significant level were used for support vector machine (SVM) classification after logistic regression analysis. The result shows that the successful prediction rate achieves as high as 81.5% which outperforms a recently published predictor (78.6%) using combination of sample entropy of three variables, inspiratory tidal volume, expiratory tidal volume, and respiration rate.