Medication recommendation aiming at accurate prescription is a significant clinical application that assists caregivers in professional practice of medicine, and obtaining informative patient representations plays an important role in building effective recommendation models. Meanwhile, conducting attentive multi-hop reading on Memory Neural Network (MemNN) that stores knowledge from previous admissions is widely applied to derive contextual patterns for accurate patient representations. However, regular attentive reading may repeatedly attend to the same slots of MemNN. Although the coverage mechanism is proposed to tackle the problem, it is based on the assumption that there is one-to-one alignment between source information and target outputs, which medical records do not follow. In pursuit of a valuable model for medication recommendation, we propose the Multi-hop Reading with Selective Coverage (MRSC). MRSC firstly conducts information selection on MemNN based on the coverage of each slot. Then the method involves coverage into the attention calculation during the multi-hop reading on MemNN, making sure that all important historical records is fully utilized by balancing attention within selected information. Experiments on real-world clinical dataset demonstrate that MRSC successfully derives informative patient representations for the recommendation by conducting selection on MemNN and limiting attention adjustment within selected information.

Medication recommendation is a significant healthcare application due to its promise in effectively prescribing medications. Avoiding fatal side effects related to Drug-Drug Interaction (DDI) is among the critical challenges. Most existing methods try to mitigate the problem by providing models with extra DDI knowledge, making models complicated. While treating all patients with different DDI properties as a single cohort would put forward strict requirements on models' generalization performance. In pursuit of a valuable model for a safe recommendation, we propose the Self-Supervised Adversarial Regularization Model for Medication Recommendation (SARMR). SARMR obtains the target distribution associated with safe medication combinations from raw patient records for adversarial regularization. In this way, the model can shape distributions of patient representations to achieve DDI reduction. To obtain accurate self-supervision information, SARMR models interactions between physicians and patients by building a key-value memory neural network and carrying out multi-hop reading to obtain contextual information for patient representations. SARMR outperforms all baseline methods in the experiment on a real-world clinical dataset. This model can achieve DDI reduction when considering the different number of DDI types, which demonstrates the robustness of adversarial regularization for safe medication recommendation.