PDF(Pubmed)
Abstract：
Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However the original definition of HUM involves multiple integration and thus a medical investigation for multi-class receiver operating characteristic (ROC) analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this article. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.
摘要：
在ROC流形（HUM）下计算大体积对于评估生物标志物区分多种疾病类型或诊断组的能力是必要的。然而，HUM的原始定义涉及多个集成，因此，当简单地实现该公式时，用于多类接收器操作特性（ROC）分析的医学研究可能会遭受巨大的计算成本。在本文中，我们介绍了一种新颖的基于图的方法来高效地计算HUM。该计算方法避免了样本数量或类别数量大时耗时的多次求和。我们进行了广泛的仿真研究，以证明我们的方法对现有R包的改进。我们将我们的方法应用于两个真实的生物医学数据集以说明其应用。
