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Using support vector machine to construct a predictive model for clinical decision-making of ventilation weaning

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5 Author(s)
Hao-Yung Yang ; Department of Health Services Administration, China Medical University, 40402 Taichung, Taiwan ; Jiin-Chyr Hsu ; Yung-Fu Chen ; Xiaoyi Jiang
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Ventilator weaning is the process of discontinuing mechanical ventilation from patients with respiratory failure. Ventilator support should be withdrawn as soon as possible when it is no longer necessary in order to reduce the likelihood of known nosocomial complications and costs. Previous investigation indicated that clinicians were often wrong when predicting weaning outcome. The motivation of this study is that although successful ventilator weaning of ICU patients has been widely studied, indicators for accurate prediction are still under investigation. The goal of this study is to find a prediction model for successful ventilator weaning using variables such physiological variables, clinical syndromes, demographic variables, and other useful information. The data obtained from 231 patients who had been supported by mechanical ventilator for longer than 21 days within the period from Nov. 2002 to Dec. 2005 were studied retrospectively. Among them, 188 patients were recruited from the period within Nov. 2002 to Dec. 2004 and the other 43 patients from Jan. 2004 to Dec. 2005. All the patients were clinically stable before being considered to undergo a weaning trial. Twenty-seven variables in total were collected with only 6 variables reaching significant level (p<0.05) were used for support vector machine (SVM) classification after statistical analysis. The results show that the constructed model is valuable in assisting clinical doctors to decide if a patient is ready to wean from the ventilator with the sensitivity, specificity, and accuracy as high as 94.74%, 95.83%, and 95.35%, respectively. Further prospective bed side test is needed to verify the efficacy of the model.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008