论文标题
结合神经网络，以改善败血症的早期诊断预测和隐私
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis
论文作者
论文摘要
结合神经网络是通过委员会决定将网络与正交属性相结合的长期技术，用于改善神经网络的概括错误。我们表明，这项技术非常适合在医疗数据上进行机器学习：首先，合奏可以平行和异步学习，从而有效地培训患者特定的组件神经网络。其次，基于选择不相关的患者特定网络来最大程度地减少概括错误的想法，我们表明，人们可以建立一个选定的特定于患者特定模型的合奏，该组合的表现优于在更大的合并数据集中训练的单个模型。第三，非著作集合组合步骤是一个最佳的低维入口点，用于应用输出扰动以确保患者特定的网络的隐私。我们使用临床专家标记的现实生活中重症监护病房数据来体现差异化合奏的差异私人合奏框架。
Ensembling neural networks is a long-standing technique for improving the generalization error of neural networks by combining networks with orthogonal properties via a committee decision. We show that this technique is an ideal fit for machine learning on medical data: First, ensembles are amenable to parallel and asynchronous learning, thus enabling efficient training of patient-specific component neural networks. Second, building on the idea of minimizing generalization error by selecting uncorrelated patient-specific networks, we show that one can build an ensemble of a few selected patient-specific models that outperforms a single model trained on much larger pooled datasets. Third, the non-iterative ensemble combination step is an optimal low-dimensional entry point to apply output perturbation to guarantee the privacy of the patient-specific networks. We exemplify our framework of differentially private ensembles on the task of early prediction of sepsis, using real-life intensive care unit data labeled by clinical experts.
