Yang Shan, Zheng Xiangwei, Ji Cun, Chen Xuanchi
School of Information Science and Engineering, Shandong Normal University, Jinan, China.
Neural Process Lett. 2021;53(2):1417-1433. doi: 10.1007/s11063-021-10449-2. Epub 2021 Feb 18.
Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient's mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers.
电子健康记录（EHRs）是与住院、诊断、用药等相关的数字记录。EHRs的二次使用可以促进临床信息学应用和医疗事业的发展。EHRs具有独特的特点，即患者就诊按时间顺序排列，但就诊内的诊断代码是随机排列的。这种层次结构需要一个多层网络来探索EHRs的不同关系信息。在本文中，我们提出了一种多层表示学习方法（MLRL），它能够通过分层探索诊断代码和患者就诊中的有价值信息来学习有效的患者表示。首先，MLRL利用多头注意力机制探索诊断代码中的潜在联系，并进行线性变换以进一步将代码向量映射到非负实值表示。然后通过汇总所有代码表示来获得初始就诊向量。其次，该方法将双向长短期记忆与自注意力机制相结合，学习加权就诊向量，这些向量聚合形成患者表示。最后，为了评估MLRL的性能，我们将其应用于真实EHRs上的患者死亡率预测，实验结果表明MLRL在预测性能上有显著提高。MLRL在曲线下面积方面达到约0.915，优于基线方法获得的结果。此外，与原始数据和其他数据表示相比，用MLRL学习到的表示在多个不同分类器上显示出其出色的结果和可用性。
