论文标题
数据驱动知识发现算法的替代辅助性能预测：应用于临床途径的进化建模
Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: application to evolutionary modeling of clinical pathways
论文作者
论文摘要
本文提出并研究了一种对数据驱动的知识发现算法的替代辅助性能预测的方法。该方法基于对替代模型的识别，用于预测目标算法的质量和性能。实施了该方法，并研究了用于在急性冠状动脉综合征患者的电子健康记录中发现可解释的临床途径簇的进化算法。几个聚类指标和执行时间分别用作目标质量和性能指标。开发了基于提出的预测算法特征和特征分析方法的分析软件原型，以提供对目标算法的性能和质量的更可解释的预测，可进一步用于参数调整。
The paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is based on the identification of surrogate models for prediction of the target algorithm's quality and performance. The proposed approach was implemented and investigated as applied to an evolutionary algorithm for discovering clusters of interpretable clinical pathways in electronic health records of patients with acute coronary syndrome. Several clustering metrics and execution time were used as the target quality and performance metrics respectively. An analytical software prototype based on the proposed approach for the prediction of algorithm characteristics and feature analysis was developed to provide a more interpretable prediction of the target algorithm's performance and quality that can be further used for parameter tuning.
