| Summary: | 博士 === 國立臺灣大學 === 醫學工程學研究所 === 107 === In their daily care of neurocritical patients, neurosurgeons and intensivists often use intracranial pressure (ICP) monitoring to understand the neurological condition of the patient, to adjust their medications, and to evaluate the prognosis. In our experience, the absolute values of ICP cannot represent the clinical situation precisely. Our study uses big data techniques such as wavelet transform, linear regression and decision tree to combine ICP and other physiological parameters such as respiratory rate, heart rate, and arterial blood pressure and establish a predictive model. We propose a novel concept to divide Cushing response into pathological, physiological and negative ones to elaborate the control of pathophysiological mechanisms by ICP. All parameters are preprocessed with the wavelet transform, thus eliminating most noises, and compressed by linear regression. After random sampling from the transformed data, a decision tree is trained to produce a predictive model whose results are well compatible with clinical situations. The model is cross-validated and the accuracy of prediction evaluated by a confusion matrix. Based on the decision tree model, a warning light system is created to predict the situation and outcome of test patients. The accuracy of outcome prediction through the model reached 81.6%. In the future, we hope to establish the model with noninvasive physiological parameters to provide advice for clinical management and possibly to replace invasive ICP monitors.
|
|---|
