In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.

中文翻译：

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使用多个生物标志物获得树分类器的最佳截止值

在生物医学实践中，多个生物标志物通常使用预先指定的分类规则与树形结构组合以进行诊断决策。树的每个节点的分类结构和截止点通常是在ad hoc上选择的依据，取决于决策者的经验。缺乏导致最佳预测性能的分析方法，并指导在预先指定的分类树中选择最佳截止点。在本文中，我们提出通过秩相关最大化的方法来搜索和估计最优决策规则。当许多生物标志物可用于分类或预测时，所提出的方法是灵活的、理论上合理的和计算上可行的。使用所提出的方法，对于预先指定的树结构分类规则，我们可以指导在树节点处选择最佳截止点，并从多个生物标志物的组合中估计最佳预测性能。