Medication recommendation is attracting enormous attention due to its promise in effectively prescribing medicines and improving the survival rate of patients. Among all challenges, drug–drug interactions (DDI) related to undesired duplication, antagonism, or alternation between drugs could lead to fatal side effects. Previous researches usually provide models with DDI knowledge to achieve DDI reduction. However, the mixed use of patients with different DDI rates places stringent requirements on the generalization performance of models. In pursuit of a more effective method, we propose the adversarially regularized model for medication recommendation (ARMR). Specifically, ARMR firstly models temporal information from medical records to obtain patient representations and builds a key-value memory network based on information from historical admissions. Then, ARMR carries out multi-hop reading on the memory network to recommend medications. Meanwhile, ARMR uses a GAN model to adversarially regulate the distribution of patient representations by matching the distribution to a desired Gaussian distribution to achieve DDI reduction. Comparative evaluations between ARMR and baselines show that ARMR outperforms all baselines in terms of medication recommendation, achieving DDI reduction regardless of numbers of DDI types being considered.

药物推荐因其有效处方药物和提高患者存活率的前景而备受关注。在所有挑战中，与不良复制，拮抗作用或药物之间交替相关的药物相互作用（DDI）可能导致致命的副作用。先前的研究通常会提供具有DDI知识的模型来实现DDI缩减。但是，具有不同DDI率的患者的混合使用对模型的泛化性能提出了严格的要求。为了寻求更有效的方法，我们提出了药物推荐的对抗性正则化模型（ARMR）。特别，ARMR首先对病历中的时间信息进行建模，以获得患者的陈述，然后根据历史入院信息建立键值存储网络。然后，ARMR在存储网络上执行多跳读取以推荐药物。同时，ARMR使用GAN模型，通过将分布与所需的高斯分布进行匹配来对抗性地调节患者代表的分布，以实现DDI降低。ARMR与基线之间的比较评估表明，就用药建议而言，ARMR优于所有基线，无论考虑哪种DDI类型，都可以降低DDI。ARMR使用GAN模型，通过将分布与所需的高斯分布进行匹配来对抗性地调节患者代表的分布，以减少DDI。ARMR与基线之间的比较评估表明，就用药建议而言，ARMR优于所有基线，无论考虑哪种DDI类型，都可以降低DDI。ARMR使用GAN模型，通过将分布与所需的高斯分布进行匹配来对抗性地调节患者代表的分布，以减少DDI。ARMR与基线之间的比较评估表明，就用药建议而言，ARMR优于所有基线，无论考虑哪种DDI类型，都可以降低DDI。