Constrained elastic net based knowledge transfer for healthcare information exchange
Constrained elastic net based knowledge transfer for healthcare information exchange
复制标题
用于医疗保健信息交换的基于约束弹性网络的知识转移
DOI：
10.1007/s10618-014-0389-3
复制
发表时间：
2015
影响因子：
4.8
通讯作者：
Reddy, Chandan K.
中科院分区：
文献类型：
--
作者：
Li, Yan;Vinzamuri, Bhanukiran;Reddy, Chandan K.
迁移学习方法已成功应用于解决各种现实问题。然而，几乎没有尝试在医疗保健应用中有效地使用这些方法。在医疗保健领域，解决迁移学习的“何时迁移”问题变得极其关键。在高度发散的源域和目标域中，迁移学习会导致负迁移。现有的迁移学习研究主要集中在从源信息中选择有用的信息以提高目标任务的绩效，但迁移学习是否能帮助目标任务以及何时将迁移学习应用于目标任务仍然是一些迫在眉睫的挑战。在本文中，我们提出了一个稀疏的特征选择模型的基础上的约束弹性网惩罚解决这个问题的“何时转移”。作为所提出的模型的案例研究，我们证明了使用糖尿病电子健康记录（EHR），其中包含来自美国所有50个州的患者记录的性能。我们的方法可以选择相关的功能，将知识从源任务转移到目标任务。该模型可以在预测模型的条件下度量多变量数据分布之间的差异，基于此度量可以避免不成功的转移。我们成功地将知识转移到不同的状态，以改善在记录不足的特定状态下的糖尿病诊断，从而在其他状态信息的帮助下建立个性化的预测模型。
Transfer learning methods have been successfully applied in solving a wide range of real-world problems. However, there is almost no attempt of effectively using these methods in healthcare applications. In the healthcare domain, it becomes extremely critical to solve the “when to transfer” issue of transfer learning. In highly divergent source and target domains, transfer learning can lead to negative transfer. Most of the existing works in transfer learning are primarily focused on selecting useful information from the source to improve the performance of the target task, but whether the transfer learning can help and when the transfer learning should be applied in the target task are still some of the impending challenges. In this paper, we address this issue of “when to transfer” by proposing a sparse feature selection model based on the constrained elastic net penalty. As a case study of the proposed model, we demonstrate the performance using the diabetes electronic health records (EHRs) which contain patient records from all fifty states in the United States. Our approach can choose relevant features to transfer knowledge from the source to the target tasks. The proposed model can measure the differences between multivariate data distributions conditional on the predicted model, and based on this measurement we can avoid unsuccessful transfer. We successfully transfer the knowledge across different states to improve the diagnosis of diabetes in a certain state with insufficient records to build an individualized predictive model with the aid of information from other states.
影响因子：
5.8
作者：
Friedman, Jerome;Hastie, Trevor;Tibshirani, Rob
通讯作者：
Tibshirani, Rob