The goal of patient-specific treatment of diseases requires a connection between clinical observations with models that are able to accurately predict the disease progression. Even when realistic models are available, it is very difficult to parameterize them and often parameter estimates that are made using early time course data prove to be highly inaccurate. Inaccuracies can cause different predictions, especially when the progression depends sensitively on the parameters. In this study, we apply a Bayesian data assimilation method, where the data are incorporated sequentially, to a model of the autoimmune disease alopecia areata that is characterized by distinct spatial patterns of hair loss. Using synthetic data as simulated clinical observations, we show that our method is relatively robust with respect to variations in parameter estimates. Moreover, we compare convergence rates for parameters with different sensitivities, varying observational times and varying levels of noise. We find that this method works better for sparse observations, sensitive parameters and noisy observations. Taken together, we find that our data assimilation, in conjunction with our biologically inspired model, provides directions for individualized diagnosis and treatments.

中文翻译：

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合成数据的数据同化作为预测斑秃疾病进展的新策略

患者特定疾病治疗的目标需要临床观察与能够准确预测疾病进展的模型之间的联系。即使有实际模型可用，也很难对其进行参数化，而且使用早期课程数据进行的参数估计通常被证明是非常不准确的。不准确会导致不同的预测，尤其是当进展敏感地取决于参数时。在这项研究中，我们将贝叶斯数据同化方法（其中数据按顺序合并）应用于以不同空间模式脱发为特征的自身免疫性疾病斑秃模型。使用合成数据作为模拟临床观察，我们表明我们的方法在参数估计的变化方面相对稳健。此外，我们比较了具有不同灵敏度、不同观察时间和不同噪声水平的参数的收敛速度。我们发现这种方法对于稀疏观测、敏感参数和噪声观测效果更好。综上所述，我们发现我们的数据同化与我们的生物启发模型相结合，为个性化诊断和治疗提供了方向。