The paper proposes an approach for surrogate-assisted tuning of knowledge discovery algorithms. The approach is based on the prediction of both the quality and performance of the target algorithm. The prediction is furtherly used as objectives for the optimization and tuning of the algorithm. The approach is investigated using clinical pathways (CP) discovery problem resolved using the evolutionary-based clustering of electronic health records (EHR). Target algorithm and the proposed approach were applied to the discovery of CPs for Acute Coronary Syndrome patients in 3434 EHRs of patients treated in Almazov National Medical Research Center (Saint Petersburg, Russia). The study investigates the possible acquisition of interpretable clusters of typical CPs within a single disease. It shows how the approach could be used to improve complex data-driven analytical knowledge discovery algorithms. The study of the results includes the feature importance of the best surrogate model and discover how the parameters of input data influence the predictions.

中文翻译：

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替代辅助知识发现算法的性能调整：在临床途径进化建模中的应用

本文提出了一种替代辅助的知识发现算法调整方法。该方法基于目标算法的质量和性能的预测。该预测进一步用作优化和调整算法的目标。使用临床途径（CP）发现问题调查了该方法，该问题通过基于进化的电子健康记录（EHR）聚类解决。将目标算法和提出的方法应用于在阿尔玛佐夫国家医学研究中心（俄罗斯圣彼得堡）治疗的3434例EHR中的急性冠脉综合征患者的CPs。该研究调查了在单一疾病中可能获得的典型CPs的可解释簇。它显示了如何使用该方法来改进复杂的数据驱动型分析知识发现算法。结果的研究包括最佳替代模型的功能重要性，并发现输入数据的参数如何影响预测。