IEEE Trans Nanobioscience. 2018 Jul;17(3):219-227. doi: 10.1109/TNB.2018.2837622. Epub 2018 May 16.
Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similarity among patients based on their historical records is a key step in personalized healthcare. The electric health records (EHRs), which are irregularly sampled and have varied patient visit lengths, cannot be directly used to measure patient similarity due to the lack of an appropriate representation. Moreover, there needs an effective approach to measure patient similarity on EHRs. In this paper, we propose two novel deep similarity learning frameworks which simultaneously learn patient representations and measure pairwise similarity. We use a convolutional neural network (CNN) to capture local important information in EHRs and then feed the learned representation into triplet loss or softmax cross entropy loss. After training, we can obtain pairwise distances and similarity scores. Utilizing the similarity information, we then perform disease predictions and patient clustering. Experimental results show that CNN can better represent the longitudinal EHR sequences, and our proposed frameworks outperform state-of-the-art distance metric learning methods.
预测患者罹患某些疾病的风险是医疗保健领域的一个重要研究课题。基于患者的历史记录准确识别和对患者相似性进行排序是个性化医疗的关键步骤。由于缺乏适当的表示，不规则采样且患者就诊时间长短不一的电子健康记录 (EHR) 不能直接用于衡量患者相似性。此外，需要一种有效的方法来衡量 EHR 上的患者相似性。在本文中，我们提出了两个新颖的深度相似性学习框架，这些框架可以同时学习患者表示并衡量两两相似度。我们使用卷积神经网络 (CNN) 来捕获 EHR 中的局部重要信息，然后将学习到的表示输入三元组损失或 softmax 交叉熵损失。训练后，我们可以获得两两距离和相似性得分。利用相似性信息，我们可以进行疾病预测和患者聚类。实验结果表明，CNN 可以更好地表示纵向 EHR 序列，并且我们提出的框架优于最先进的距离度量学习方法。
