标题： 从深度网络中提炼知识及其在医疗领域中的应用 显示英文标题

标题： Distilling Knowledge from Deep Networks with Applications to Healthcare Domain

摘要： 电子健康记录（EHR）的指数增长为计算表型研究中发现有意义的数据驱动表示和疾病模式带来了新的机遇和紧迫需求。深度学习模型在计算表型任务中的稳健预测方面表现出色，但面临模型可解释性的问题，这对于参与决策的临床医生至关重要。在本文中，我们引入了一种称为可解释模仿学习的新知识蒸馏方法，以学习可解释的表型特征，在模仿深度学习模型性能的同时进行稳健预测。我们的框架使用梯度提升树从深度学习模型（如堆叠去噪自动编码器和长短期记忆网络）中学习可解释特征。在真实世界临床时间序列数据集上的全面实验表明，我们的方法在性能上与深度学习模型相当或更好，并为临床决策提供了可解释的表型。 显示英文摘要

摘要： Exponential growth in Electronic Healthcare Records (EHR) has resulted in new opportunities and urgent needs for discovery of meaningful data-driven representations and patterns of diseases in Computational Phenotyping research. Deep Learning models have shown superior performance for robust prediction in computational phenotyping tasks, but suffer from the issue of model interpretability which is crucial for clinicians involved in decision-making. In this paper, we introduce a novel knowledge-distillation approach called Interpretable Mimic Learning, to learn interpretable phenotype features for making robust prediction while mimicking the performance of deep learning models. Our framework uses Gradient Boosting Trees to learn interpretable features from deep learning models such as Stacked Denoising Autoencoder and Long Short-Term Memory. Exhaustive experiments on a real-world clinical time-series dataset show that our method obtains similar or better performance than the deep learning models, and it provides interpretable phenotypes for clinical decision making.