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A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression

机译:通过支持向量回归预测重症监护病房肝移植患者他克莫司血药浓度的新方法

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摘要

IntroductionTacrolimus is an important immunosuppressive drug for organ transplantation patients. It has a narrow therapeutic range, toxic side effects, and a blood concentration with wide intra- and interindividual variability. Hence, it is of the utmost importance to monitor tacrolimus blood concentration, thereby ensuring clinical effect and avoiding toxic side effects. Prediction models for tacrolimus blood concentration can improve clinical care by optimizing monitoring of these concentrations, especially in the initial phase after transplantation during intensive care unit (ICU) stay. This is the first study in the ICU in which support vector machines, as a new data modeling technique, are investigated and tested in their prediction capabilities of tacrolimus blood concentration. Linear support vector regression (SVR) and nonlinear radial basis function (RBF) SVR are compared with multiple linear regression (MLR).
机译:简介他克莫司是器官移植患者的一种重要的免疫抑制药物。它具有较窄的治疗范围,毒性副作用以及血液浓度,个体和个体之间的变异性很大。因此,监测他克莫司的血药浓度,从而确保临床效果并避免毒性副作用至关重要。他克莫司血药浓度的预测模型可通过优化对这些浓度的监测来改善临床护理,尤其是在重症监护病房(ICU)停留期间移植后的初始阶段。这是ICU中的第一项研究,其中对支持向量机作为一种新的数据建模技术进行了研究,并测试了他克莫司血药浓度的预测能力。将线性支持向量回归(SVR)和非线性径向基函数(RBF)SVR与多元线性回归(MLR)进行比较。

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