Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathways

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 predic-tion of the target algorithm's quality and performance. The proposed approach was implemented and investi-gated 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 devel-oped to provide a more interpretable prediction of the target algorithm's performance and quality that can be further used for parameter tuning.

Automated summary

This paper proposes and investigates an approach for surrogate-assisted performance prediction of data-driven knowledge discovery algorithms. The approach is applied to the discovery of clinical pathways and uses surrogate models to predict the quality and performance of the target algorithm. An analytical software prototype is developed to provide a more interpretable prediction of the algorithm's performance and quality.