论文标题
校准预测模型的局部解释性：2型糖尿病的病例Mellitus筛查测试
Local Interpretability of Calibrated Prediction Models: A Case of Type 2 Diabetes Mellitus Screening Test
论文作者
论文摘要
机器学习（ML）模型通常是复杂的且由于其“黑色盒子”特征而难以解释。 ML模型的可解释性通常定义为人类可以理解ML模型达成的决策原因的程度。在许多医疗领域，由于基于ML模型的决策，可解释性在许多医疗保健领域都非常重要。 ML模型输出的校准是ML模型在实践中通常会忽略的另一个问题。本文代表了对预测模型校准对结果解释性的影响的早期工作。我们提出了一种患者在糖尿病筛查预测情景中的用例，并使用三种不同的技术可视化结果，以证明校准和未校准的正则回归模型之间的差异。
Machine Learning (ML) models are often complex and difficult to interpret due to their 'black-box' characteristics. Interpretability of a ML model is usually defined as the degree to which a human can understand the cause of decisions reached by a ML model. Interpretability is of extremely high importance in many fields of healthcare due to high levels of risk related to decisions based on ML models. Calibration of the ML model outputs is another issue often overlooked in the application of ML models in practice. This paper represents an early work in examination of prediction model calibration impact on the interpretability of the results. We present a use case of a patient in diabetes screening prediction scenario and visualize results using three different techniques to demonstrate the differences between calibrated and uncalibrated regularized regression model.
