Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
Removing Confounding Factors Associated Weights in Deep Neural Networks Improves the Prediction Accuracy for Healthcare Applications
复制标题
DOI：
10.1101/442442
复制
发表时间：
2018-03
影响因子：
--
通讯作者：
Haohan Wang;Zhenglin Wu;E. Xing
中科院分区：
文献类型：
--
作者：
Haohan Wang;Zhenglin Wu;E. Xing
医疗保健数据的激增为应用数据驱动的方法（如机器学习方法）来辅助诊断带来了机会。最近，许多深度学习方法在使用原始输入数据预测疾病状态方面取得了令人印象深刻的成功。然而，深度学习的“黑箱”性质和生物医学应用的高可靠性要求，为混杂因素的存在带来了新的挑战。本文简要论述了混淆因素处理不当会导致模型在实际应用中的次优性能，提出了一种有效的方法，可以去除年龄或性别等混杂因素的影响，以提高神经网络的跨队列预测精度。我们的方法的一个明显优势是，它只需要对基线模型的架构进行最小的更改，因此它可以插入大多数现有的神经网络。我们利用卷积神经网络和LSTM对ct扫描、MRA和EEG脑电波进行了实验，验证了该方法的有效性。
The proliferation of healthcare data has brought the opportunities of applying data-driven approaches, such as machine learning methods, to assist diagnosis. Recently, many deep learning methods have been shown with impressive successes in predicting disease status with raw input data. However, the “black-box” nature of deep learning and the high-reliability requirement of biomedical applications have created new challenges regarding the existence of confounding factors. In this paper, with a brief argument that inappropriate handling of confounding factors will lead to models’ sub-optimal performance in real-world applications, we present an efficient method that can remove the influences of confounding factors such as age or gender to improve the across-cohort prediction accuracy of neural networks. One distinct advantage of our method is that it only requires minimal changes of the baseline model’s architecture so that it can be plugged into most of the existing neu-ral networks. We conduct experiments across CT-scan, MRA, and EEG brain wave with convolutional neural networks and LSTM to verify the efficiency of our method.