Patient-specific models for diagnostics and treatment planning require reliable parameter estimation and model predictions. Mathematical models of physiological systems are often formulated as systems of nonlinear ordinary differential equations with many parameters and few options for measuring all state variables. Consequently, it can be difficult to determine which parameters can reliably be estimated from available data. This investigation highlights pitfalls associated with practical parameter identifiability and subset selection. The latter refer to the process associated with selecting a subset of parameters that can be identified uniquely by parameter estimation protocols. The methods will be demonstrated using five examples of increasing complexity, as well as with patient-specific model predicting arterial blood pressure. This study demonstrates that methods based on local sensitivities are preferable in terms of computational cost and model fit when good initial parameter values are available, but that global methods should be considered when initial parameter value is not known or poorly understood. For global sensitivity analysis, Morris screening provides results in terms of parameter sensitivity ranking at a much lower computational cost.

中文翻译：

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数学生物学问题的参数子集选择技术。

用于诊断和治疗计划的患者特定模型需要可靠的参数估计和模型预测。生理系统的数学模型通常被公式化为非线性常微分方程系统，该系统具有许多参数并且很少用于测量所有状态变量。因此，可能难以确定可以从可用数据可靠地估计出哪些参数。这项研究突出了与实际参数可识别性和子集选择相关的陷阱。后者是指与选择可以由参数估计协议唯一标识的参数子集相关的过程。将使用五个复杂度不断提高的示例以及预测动脉血压的患者特定模型来演示该方法。这项研究表明，当可获得良好的初始参数值时，就计算成本和模型拟合而言，基于局部敏感性的方法更可取，但当初始参数值未知或理解不充分时，应考虑使用全局方法。对于全局灵敏度分析，Morris筛查以参数灵敏度排名的方式提供了较低的计算成本。