贝叶斯数据同化支持个体化化疗中的知情决策
Bayesian data assimilation to support informed decision-making in individualised chemotherapy
摘要：
治疗药物/生物标志物监测（TDM）的一个重要组成部分是将患者数据与先验知识相结合，以基于模型预测治疗结果。目前的贝叶斯预测工具通常只依赖于最可能的模型参数（最大后验概率（MAP）估计）。然而，这种基于MAP的方法既不一定预测最可能的结果，也不量化治疗无效或毒性的风险。贝叶斯数据同化（DA）方法克服了这些限制，提供了一个全面的不确定性量化。我们比较DA的方法与MAP为基础的方法，并显示如何概率的声明化疗诱导的中性粒细胞减少症相关的关键标志物可以利用更多的信息决策支持个性化化疗。序贯贝叶斯DA被证明是最有效的处理病例间变异和整合TDM数据的计算。对于能够更频繁地收集数据的新型数字监护设备，这些功能对于改善各种治疗领域的患者护理决策至关重要。
英文摘要：
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a-posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualised chemotherapy. Sequential Bayesian DA proved to be most computational efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
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