Bosan S, Harris T R
Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Ann Biomed Eng. 1996 Jan-Feb;24(1):124-38. doi: 10.1007/BF02771001.
In order to successfully use a model for parameter identification, it must be carefully analyzed. Current analysis methods, however, are ad hoc and provide only partial information. We extended these methods through the application of stacked dimensions, a scientific visualization method. The end result of our extensions are multi-dimensional parametric model-images. These images depict a model as a function of all its parameters in a single graphic. We applied parametric model-images to model verification (behavioral analysis), sensitivity analysis, and identifiability analysis. We applied our methodology to the evaluation of pulmonary vascular capillary-transport models. Results have shown that the visualization-based method provides a more complete view of a model's behavior and its other characteristics. Furthermore, our method has also proven to be more computationally efficient than the traditional approaches.
为了成功地使用模型进行参数识别，必须对其进行仔细分析。然而，当前的分析方法是临时的，只提供部分信息。我们通过应用堆叠维度（一种科学可视化方法）扩展了这些方法。我们扩展的最终结果是多维参数模型图像。这些图像在单个图形中描绘了模型作为其所有参数的函数。我们将参数模型图像应用于模型验证（行为分析）、敏感性分析和可识别性分析。我们将我们的方法应用于肺血管毛细血管运输模型的评估。结果表明，基于可视化的方法能更全面地展示模型的行为及其其他特征。此外，我们的方法在计算上也比传统方法更高效。
