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Reliable prediction of anti-diabetic drug failure using a reject option

机译：使用拒绝选项可靠预测抗糖尿病药物失败

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摘要

The medical care for patients with type 2 diabetes generally involves ingestion of oral hypoglycemic agents in order to lower their glucose level. When predicting the result of the medication using a classification approach, high prediction accuracy of the classifier is essential because of high misclassification costs. The application of a reject option to this approach supports more accurate prediction, allowing for human experts to examine when the classifier is unreliable to predict. In this paper, we propose a reject option framework based on heterogeneous ensemble learning through a two-phase fusion. The first phase is to calculate confidence scores, which are used to determine whether to predict, and the second phase is to derive final prediction results by fusing the outputs from multiple heterogeneous classifiers. We confirm the effectiveness of the proposed method to the anti-diabetic drug failure prediction problem through experiments on actual electronic medical records data of type 2 diabetes. The proposed method yields a better trade-off between accuracy and rejection than other reject options with statistical significance. A lower prediction error is obtained for the same degree of rejection. We obtained desirable accuracy for the anti-diabetic drug failure problem by applying the proposed reject option, which allows using the classification approach in practice. The accurate prediction of drug failure at the moment of prescription can assist clinical decisions for patients. In addition, in-depth analysis can be considered for those prescriptions that are predicted as failure or rejected.
机译：对2型糖尿病患者的医疗护理通常涉及口服降糖药，以降低其血糖水平。当使用分类方法预测药物的结果时，分类器的高预测精度至关重要，因为分类错误的成本很高。将拒绝选项应用于此方法可支持更准确的预测，从而使人类专家可以检查何时分类器不可靠进行预测。在本文中，我们提出了一种通过两阶段融合基于异类集成学习的拒绝选项框架。第一阶段是计算置信度分数，用于确定是否进行预测，第二阶段是通过融合多个异构分类器的输出来得出最终预测结果。通过对2型糖尿病的实际电子病历数据进行实验，我们证实了该方法对抗糖尿病药物失败预测问题的有效性。与具有统计意义的其他剔除选项相比，所提出的方法在精度和剔除之间产生了更好的折衷。对于相同的拒绝度，可以获得较低的预测误差。通过应用建议的拒绝选项，我们获得了抗糖尿病药物失败问题的理想准确性，该选项允许在实践中使用分类方法。处方时对药物失败的准确预测可以帮助患者做出临床决策。另外，可以考虑对那些被预测为失败或被拒绝的处方进行深入分析。

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