GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
This work addresses the need for safe and personalized medication recommendations in healthcare, particularly for patients with complex conditions, by reducing adverse drug interactions, though it is incremental in improving existing methods.
The paper tackled the problem of recommending medication combinations for patients with complex health conditions by proposing GAMENet, which integrates drug-drug interaction knowledge graphs and patient history to provide personalized and safe recommendations, resulting in a 3.60% reduction in DDI rate compared to baselines.
Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.