Institute for Laboratory Animal Science & Experimental Surgery, RWTH Aachen University, Faculty of Medicine, Pauwelsstraße 30, 52074 Aachen, Germany.
J Biomed Inform. 2021 Jan;113:103625. doi: 10.1016/j.jbi.2020.103625. Epub 2020 Nov 19.
To develop and evaluate methods to assess single and grouped variables impact on measuring intervention severities and support a search for most expressive variables.
Datasets of cohort studies are analyzed automatically based on algorithms. For this, a metric is developed to compare measured variables in different cohorts in a data-mining process. Variables are measured in all possible combinations to detect possible synergies of certain variable constellations and allow for a ranking of the combinations' expressiveness. Such ranking serves as a basis for a wide range of algorithmic data analysis. In an exemplary application, every group member's impact on the total result is determined based on the principle of the cooperative game theory besides to the total expressiveness of the variable groups.
For different types of interventions, the method is applied to experimental data containing multiple recorded medical lab values. The expressiveness of variable combinations to indicate severity is ranked by means of a metric. Within each combination, any variable's contribution to the total effect is determined and accumulated over whole datasets to yield local and global variable importance measures. The computed results have been successfully matched with clinical expectations to prove their plausibility.
Algorithmic evaluation shows to be a promising approach in automatized quantification of variable expressiveness. It can assess descriptive power of measurements, help to improve future study designs and expose worthwhile research issues.
开发和评估方法，以评估单一和分组变量对测量干预严重程度的影响，并支持寻找最具表达力的变量。
基于算法自动分析队列研究数据集。为此，开发了一种度量标准，用于在数据挖掘过程中比较不同队列中测量的变量。以所有可能的组合来测量变量，以检测某些变量组合的可能协同作用，并对组合的表达能力进行排序。这种排序为广泛的算法数据分析提供了基础。在一个示例应用中，根据合作博弈理论的原理，除了变量组的总表达能力外，还确定了每个组内成员对总结果的影响。
针对不同类型的干预，该方法应用于包含多个记录的医学实验室值的实验数据。通过度量标准对指示严重程度的变量组合的表达能力进行排序。在每个组合中，确定任何变量对总效果的贡献，并在整个数据集上进行累积，以得出局部和全局变量重要性度量。计算结果与临床预期相匹配，证明了其合理性。
算法评估被证明是一种很有前途的自动量化变量表达能力的方法。它可以评估测量的描述能力，有助于改进未来的研究设计，并揭示有价值的研究问题。
