Diagnosis prediction is an important predictive task in health care that aims to predict the patient future diagnosis based on their historical medical records. A crucial requirement for this task is to effectively model the high-dimensional, noisy, and temporal electronic health record (EHR) data. Existing studies fulfill this requirement by applying recurrent neural networks with attention mechanisms, but facing data insufficiency and noise problem. Recently, more accurate and robust medical knowledge-guided methods have been proposed and have achieved superior performance. These methods inject the knowledge from a graph structure medical ontology into deep models via attention mechanisms to provide supplementary information of the input data. However, these methods only partially leverage the knowledge graph and neglect the global structure information, which is an important feature. To address this problem, we propose an end-to-end robust solution, namely Graph Neural Network-Based Diagnosis Prediction (GNDP). First, we propose to utilize the medical knowledge graph as an internal information of a patient by constructing sequential patient graphs. These graphs not only carry the historical information from the EHR but also infuse with domain knowledge. Then we design a robust diagnosis prediction model based on a spatial-temporal graph convolutional network. The proposed model extracts meaningful features from sequential graph EHR data effectively through multiple spatial-temporal graph convolution units to generate robust patients' representations for accurate diagnosis predictions. We evaluate the performance of GNDP against a set of state-of-the-art methods on two real-world medical data sets, the results demonstrate that our methods can achieve a better utilization of knowledge graph and improve the accuracy on diagnosis prediction tasks.

中文翻译：

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基于图神经网络的诊断预测。

诊断预测是医疗保健中一项重要的预测任务，旨在根据患者的历史病历预测患者未来的诊断。此任务的一个关键要求是有效地对高维、嘈杂和时间的电子健康记录 (EHR) 数据进行建模。现有研究通过应用具有注意力机制的循环神经网络来满足这一要求，但面临数据不足和噪声问题。最近，已经提出了更准确和强大的医学知识引导方法，并取得了优异的性能。这些方法通过注意力机制将来自图结构医学本体的知识注入深度模型，以提供输入数据的补充信息。然而，这些方法仅部分利用知识图谱而忽略了全局结构信息，这是一个重要特征。为了解决这个问题，我们提出了一种端到端的鲁棒解决方案，即基于图神经网络的诊断预测（GNDP）。首先，我们建议通过构建连续的患者图来利用医学知识图作为患者的内部信息。这些图不仅携带来自 EHR 的历史信息，而且还注入了领域知识。然后我们设计了一个基于时空图卷积网络的鲁棒诊断预测模型。所提出的模型通过多个时空图卷积单元有效地从序列图 EHR 数据中提取有意义的特征，以生成稳健的患者表征以进行准确的诊断预测。