RASNet: Recurrent aggregation neural network for safe and efficient drug recommendation

Drug recommendation is one of the most crucial research topics in smart healthcare. Its goal is to provide a set of safe drug combination based on the patient’s electronic health records (EHRs). Drug recommendation is challenging because it is difficult to obtain an appropriate representation of patient’s health state from these personalized historical records. Meanwhile, drug recommendation must prioritize the safety of drug combination because drug–drug interactions (DDIs) could result in side effects. To address these issues, this paper proposes a novel recurrent aggregation neural network for safe drug recommendation, called RASNet. RASNet introduces a straightforward but efficient recurrent aggregation neural network to capture historical records related to the patient’s health state of the current visit, which could improve the performance of EHR-based personalized modeling, particularly in cases where the patient’s condition changes periodically. Furthermore, this paper presents a novel exponential controller for DDI loss to enhance the safety of drug combination. The proposed controller not only balances the DDI rate between the safety and accuracy of the drug recommendation but also ensures the performance even when the DDI rate is low. Extensive experiments on the MIMIC-III dataset demonstrate that RASNet achieves state-of-the-art performance. Moreover, RASNet exhibits excellent efficiency and safety in drug recommendation. [Display omitted] •Drug recommendation aims to suggest effective and safe drugs based on the patients’ medical history records.•RASNet could address the noisy data problem caused by periodic changes due to chronic diseases.•The exponential controller of drug–drug interaction loss could ensure the safety and accuracy of drug recommendation.•RASNet demonstrates outstanding accuracy and efficiency in drug recommendation.