[论文解读] Adversarial Multi-Source Transfer Learning in Healthcare: Application to Glucose Prediction for Diabetic People

Maxime De Bois, Mounîm A. El‐Yacoubi+1 more|arXiv (Cornell University)|Jun 29, 2020

Artificial Intelligence in Healthcare参考文献 44被引用 57

一句话总结

本文提出一种多源对抗式迁移学习框架用于医疗保健，并将其应用于糖尿病患者的个性化葡萄糖预测。

ABSTRACT

Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning. To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable. We apply this idea to glucose forecasting for diabetic people using a fully convolutional neural network. The evaluation is done by exploring various transfer scenarios with three datasets characterized by their high inter and intra variability. While transferring knowledge is beneficial in general, we show that the statistical and clinical accuracies can be further improved by using of the adversarial training methodology, surpassing the current state-of-the-art results. In particular, it shines when using data from different datasets, or when there is too little data in an intra-dataset situation. To understand the behavior of the models, we analyze the learnt feature representations and propose a new metric in this regard. Contrary to a standard transfer, the adversarial transfer does not discriminate the patients and datasets, helping the learning of a more general feature representation. The adversarial training framework improves the learning of a general feature representation in a multi-source environment, enhancing the knowledge transfer to an unseen target. The proposed method can help improve the efficiency of data shared by different health actors in the training of deep models.

研究动机与目标

• 阐明在异构医疗数据源之间进行鲁棒知识迁移的必要性。
• 提出一种利用多源信息来提升葡萄糖预测的对抗式迁移学习方法。
• 在2型糖尿病背景下评估该框架，以提升预测准确性和安全性。

提出的方法

• 开发一个使用对抗训练来对齐来自多个数据源的表示的框架。
• 利用迁移学习在没有目标源标注数据的情况下，将模型适应到新患者或队列。
• 结合领域对抗目标，以在保留预测信号的同时减少源特定偏差。
• 将该方法建立在现有的迁移学习和领域自适应文献基础上，以证明其设计的合理性。

实验结果

研究问题

• RQ1 对抗式多源迁移学习是否能够在异构医疗数据集中提升葡萄糖预测？
• RQ2 所提出的方法与单源或非对抗性迁移学习方法在血糖预测中的对比如何？
• RQ3 多源整合对糖尿病患者预测的安全性和可靠性有何影响？

主要发现

• 该框架通过利用来自多个源的信息，显示出改进的预测能力。
• 对抗性对齐有助于减轻源特定偏差，促进对新个体或队列的迁移。
• 该方法位于已建立的迁移学习和领域对抗训练文献中。
• 该工作提供了实用性的指南和用于复现实验的代码资源，以再现葡萄糖预测实验。