Carolina Health Informatics Program, University of North Carolina, Chapel Hill, North Carolina.
Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.
Stud Health Technol Inform. 2022 Jun 6;290:460-464. doi: 10.3233/SHTI220118.
Chart checking is a time intensive process with high cognitive workload for physicists. Previous studies have partially automated and standardized chart checking, but limited studies implement data-driven approaches to reduce cognitive workload for quality assurance processes. This study aims to evaluate feature selection methods to improve the interpretability and transparency of machine learning models in predicting the degree of difficulty for a pretreatment physics chart check. We compare chi-square, mutual information, feature importance thresholding, and greedy feature selection for four different classifiers. Random forest has the highest performance with SMOTE oversampling using mutual information for feature selection (accuracy 84.0%, AUC 87.0%, precision 80.0%, recall 80.0%). This study demonstrates that feature selection methods can improve model interpretability and transparency.
图表检查对于物理学家来说是一个耗时且认知负担高的过程。先前的研究已经部分实现了图表检查的自动化和标准化，但很少有研究采用数据驱动的方法来降低质量保证过程的认知负担。本研究旨在评估特征选择方法，以提高机器学习模型在预测预处理物理图表检查难度程度方面的可解释性和透明度。我们比较了卡方检验、互信息、特征重要性阈值和贪婪特征选择这四种不同的分类器。在使用互信息进行特征选择的 SMOTE 过采样的情况下，随机森林的性能最高（准确率 84.0%，AUC 87.0%，精确率 80.0%，召回率 80.0%）。本研究表明，特征选择方法可以提高模型的可解释性和透明度。
