Deep learning-based predictive models, leveraging Electronic Health Records
(EHR), are receiving increasing attention in healthcare. An effective
representation of a patient's EHR should hierarchically encompass both the
temporal relationships between historical visits and medical events, and the
inherent structural information within these elements. Existing patient
representation methods can be roughly categorized into sequential
representation and graphical representation. The sequential representation
methods focus only on the temporal relationships among longitudinal visits. On
the other hand, the graphical representation approaches, while adept at
extracting the graph-structured relationships between various medical events,
fall short in effectively integrate temporal information. To capture both types
of information, we model a patient's EHR as a novel temporal heterogeneous
graph. This graph includes historical visits nodes and medical events nodes. It
propagates structured information from medical event nodes to visit nodes and
utilizes time-aware visit nodes to capture changes in the patient's health
status. Furthermore, we introduce a novel temporal graph transformer (TRANS)
that integrates temporal edge features, global positional encoding, and local
structural encoding into heterogeneous graph convolution, capturing both
temporal and structural information. We validate the effectiveness of TRANS
through extensive experiments on three real-world datasets. The results show
that our proposed approach achieves state-of-the-art performance.

利用深度学习的预测模型，基于电子病历（EHR），在医疗保健领域备受关注。我们提出了一种新颖的时间异构图模型，将病人的 EHR 建模成历史访问节点和医疗事件节点，并利用时间感知访问节点来捕捉患者健康状况的变化，同时通过将时间边特征、全局位置编码和局部结构编码整合到异构图卷积中，捕捉了时间和结构信息。通过对三个真实数据集进行广泛实验，我们证实了 TRANS 的有效性，结果表明我们提出的方法达到了最先进的性能。

使用时间图形表示对电子健康记录进行预测建模

Predictive Modeling with Temporal Graphical Representation on Electronic  Health Records

Many of diverse phenomena in nature often inherently encode both short and
long term temporal dependencies, short term dependencies especially resulting
from the direction of flow of time. In this respect, we discovered experimental
evidences suggesting that {\it interrelations} of these events are higher for
closer time stamps. However, to be able for attention based models to learn
these regularities in short term dependencies, it requires large amounts of
data which are often infeasible. This is due to the reason that, while they are
good at learning piece wised temporal dependencies, attention based models lack
structures that encode biases in time series. As a resolution, we propose a
simple and efficient method that enables attention layers to better encode
short term temporal bias of these data sets by applying learnable, adaptive
kernels directly to the attention matrices. For the experiments, we chose
various prediction tasks using Electronic Health Records (EHR) data sets since
they are great examples that have underlying long and short term temporal
dependencies. The results of our experiments show exceptional classification
results compared to best performing models on most of the task and data sets.

我们提出了一种简单而高效的方法，通过在注意力矩阵上直接应用可学习的自适应卷积核，使注意力层能更好地编码这些数据集的短期时间偏差。在实验中，我们选择了使用电子健康记录（EHR）数据集的各种预测任务，因为它们是具有底层长期和短期时间依赖性的绝佳示例。我们的实验结果表明，在大多数任务和数据集上，与表现最佳的模型相比，我们的分类结果非常出色。

自我注意力与时间先验：我们能从时间的箭头中学到更多吗？

Self Attention with Temporal Prior: Can We Learn More from Arrow of  Time?

Medication recommendation using Electronic Health Records (EHR) is
challenging due to complex medical data. Current approaches extract
longitudinal information from patient EHR to personalize recommendations.
However, existing models often lack sufficient patient representation and
overlook the importance of considering the similarity between a patient's
medication records and specific medicines. Therefore, an Attention-guided
Collaborative Decision Network (ACDNet) for medication recommendation is
proposed in this paper. Specifically, ACDNet utilizes attention mechanism and
Transformer to effectively capture patient health conditions and medication
records by modeling their historical visits at both global and local levels.
ACDNet also employs a collaborative decision framework, utilizing the
similarity between medication records and medicine representation to facilitate
the recommendation process. The experimental results on two extensive medical
datasets, MIMIC-III and MIMIC-IV, clearly demonstrate that ACDNet outperforms
state-of-the-art models in terms of Jaccard, PR-AUC, and F1 score, reaffirming
its superiority. Moreover, the ablation experiments provide solid evidence of
the effectiveness of each module in ACDNet, validating their contribution to
the overall performance. Furthermore, a detailed case study reinforces the
effectiveness of ACDNet in medication recommendation based on EHR data,
showcasing its practical value in real-world healthcare scenarios.

提出了一种基于 EHR 数据的注意力引导协同决策网络 (ACDNet) 进行药物推荐的方法，ACDNet 利用注意力机制和 Transformer 模型，全面、局部地建模患者的健康状况和药物记录，并利用药物记录与药物表示之间的相似性促进推荐过程；实验结果表明，ACDNet 在 Jaccard、PR-AUC 和 F1 分数等指标上优于最先进的模型，并且消融实验进一步验证了 ACDNet 每个模块的有效性，实证了其在基于 EHR 数据的药物推荐中的实用性。