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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Monitoring of anticoagulant therapy applying a dynamic statistical model
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Monitoring of anticoagulant therapy applying a dynamic statistical model

机译：使用动态统计模型监测抗凝治疗

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摘要

Patients with an increased risk of thrombosis may require treatment with vitamin K-antagonists such as warfarin. Treatment with warfarin has been reported difficult mainly due to high inter- and intraindividual variability in response to the drug [1]. Using predictive models that can predict International Normalised Ratio (INR) values enables for a higher degree of individualised warfarin dosing regime. This paper reports the outcome of the development of a dynamic prediction model. It takes warfarin intake and INR values as inputs, and uses an individual sensitivity parameter to model response to warfarin intake. The model is set on state-space form and uses Kalman filtering technique to optimise individual parameters. Retrospective test of the model proved robustness to choices of initial parameters, and feasible prediction results of both INR values and suggested warfarin dosage, which may prove beneficial for both patients and healthcare takers.
机译：血栓形成风险增加的患者可能需要使用维生素K拮抗剂（如华法林）进行治疗。据报道，使用华法林治疗困难主要是由于对药物的反应之间和个体之间的高度差异性[1]。使用可以预测国际标准化比率（INR）值的预测模型可以实现更高程度的个性化华法林剂量方案。本文报告了动态预测模型开发的结果。它以华法林摄入量和INR值作为输入，并使用单个敏感性参数来建模对华法林摄入量的反应。该模型以状态空间形式设置，并使用卡尔曼滤波技术优化单个参数。该模型的回顾性测试证明了对初始参数选择的鲁棒性，以及INR值和建议的华法林剂量的可行预测结果，这可能对患者和医疗保健者均有益。

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