The widespread application of Electronic Health Records (EHR) data in the
medical field has led to early successes in disease risk prediction using deep
learning methods. These methods typically require extensive data for training
due to their large parameter sets. However, existing works do not exploit the
full potential of EHR data. A significant challenge arises from the infrequent
occurrence of many medical codes within EHR data, limiting their clinical
applicability. Current research often lacks in critical areas: 1) incorporating
disease domain knowledge; 2) heterogeneously learning disease representations
with rich meanings; 3) capturing the temporal dynamics of disease progression.
To overcome these limitations, we introduce a novel heterogeneous graph
learning model designed to assimilate disease domain knowledge and elucidate
the intricate relationships between drugs and diseases. This model innovatively
incorporates temporal data into visit-level embeddings and leverages a
time-aware transformer alongside an adaptive attention mechanism to produce
patient representations. When evaluated on two healthcare datasets, our
approach demonstrated notable enhancements in both prediction accuracy and
interpretability over existing methodologies, signifying a substantial
advancement towards personalized and proactive healthcare management.

通过利用电子健康记录 (EHR) 数据在医学领域的广泛应用，使用深度学习方法在疾病风险预测方面取得了早期成功。然而，现有研究对 EHR 数据的潜力未能充分利用，存在着许多关键问题，如融入疾病领域知识、异构学习疾病表示以及捕捉疾病进展的时间动态等。为了克服这些限制，本研究引入了一种新型的异构图学习模型，旨在融合疾病领域知识，阐明药物和疾病之间错综复杂的关系。该模型创新性地将时间数据融入到访问级嵌入中，并利用一个考虑时间的 Transformer 和自适应注意机制来生成患者表示。经过两个医疗数据集的评估，我们的方法在预测准确性和可解释性方面表现出显著提升，为个性化和主动式的医疗管理迈出了实质性的进展。