Recent years have witnessed the rapid accumulation of massive electronic
medical records (EMRs), which highly support the intelligent medical services
such as drug recommendation. However, prior arts mainly follow the traditional
recommendation strategies like collaborative filtering, which usually treat
individual drugs as mutually independent, while the latent interactions among
drugs, e.g., synergistic or antagonistic effect, have been largely ignored. To
that end, in this paper, we target at developing a new paradigm for drug
package recommendation with considering the interaction effect within drugs, in
which the interaction effects could be affected by patient conditions.
Specifically, we first design a pre-training method based on neural
collaborative filtering to get the initial embedding of patients and drugs.
Then, the drug interaction graph will be initialized based on medical records
and domain knowledge. Along this line, we propose a new Drug Package
Recommendation (DPR) framework with two variants, respectively DPR on Weighted
Graph (DPR-WG) and DPR on Attributed Graph (DPR-AG) to solve the problem, in
which each the interactions will be described as signed weights or attribute
vectors. In detail, a mask layer is utilized to capture the impact of patient
condition, and graph neural networks (GNNs) are leveraged for the final graph
induction task to embed the package. Extensive experiments on a real-world data
set from a first-rate hospital demonstrate the effectiveness of our DPR
framework compared with several competitive baseline methods, and further
support the heuristic study for the drug package generation task with adequate
performance.

本研究提出了一种考虑药物之间相互作用，及其与患者病况影响的药物推荐新方法，其中包括在电子病历和领域知识上设计的药物交互图和两个变体的 Drug Package Recommendation (DPR) 框架，该框架采用了图神经网络 (GNN) 进行最终的推荐任务，实验结果表明该方法优于几个竞争基准方法，具有充分表现的药品生成任务的启发性研究支持。