DMRNet: Effective Network for Accurate Discharge Medication Recommendation

Electronic Health Records, which contain abundant structured data information of the patients, can help clinicians and data scientists address complex medical issues, particularly medication recommendation. The recommendation of medications is crucial for accurate and timely prescriptions. It is a nuanced task that entails analyzing various sources of healthcare data. Traditional medication recommendation is performed manually, which is labor-intensive and error-prone. The development of Electronic Health Records enables automatic medication recommendation. There are mainly two categories of methods for automatic medication recommendation. The first category uses the patients' current visit information and the drug-drug interactions (DDI). For these methods, both the comprehensive patient's medical history and the significant medication-diagnosis knowledge are not exploited appropriately. The second category utilizes longitudinal patient data, but different history visits are incorporated indiscriminately. Furthermore, in clinical practice, the associations between historical medications and future prescriptions are highlighted. However, they are less emphasized in current methods. Nevertheless, this is less emphasized by current automatic medication recommendation methods. To tackle the above challenges, we propose a three-module Discharge Medication Recommendation Network, called DMRNet, for accurate discharge medication recommendations. Specifically, the Information Integration Module combines information from the current visit and significant external knowledge e.g., the Diagnosis-Medication Co-occurrence (DMC) relationship. The Medication Retention Module is specially designed to capture the associations between the historical medications and the recommended medications. The History Retrieval Module differentiates the significance of different historical visits and incorporates them based on different significance values. Experimental evaluations on benchmark datasets, i.e., MIMIC-III and MIMIC-IV, confirm DMRNet's superiority over state-of-the-art baseline methods in terms of Jaccard Similarity, F1-score, Precision and Recall.