Abstract
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.
Abstract (translated)
深度学习的最新进展正在彻底改变医疗领域，包括提供药物建议的解决方案，特别是为复杂健康状况的患者推荐药物组合。现有方法要么不根据患者健康史进行定制，要么忽略可能导致不良后果的药物相互作用（DDI）的现有知识。为了填补这一空白，我们提出了图形增强记忆网络（GAMENet），它通过作为图形卷积网络实现的存储器模块集成药物 - 药物相互作用知识图，并将纵向患者记录建模为查询。它经过端到端的培训，可提供安全，个性化的药物组合推荐。我们通过与真实EHR数据的几种最先进的方法进行比较，证明了GAMENet的有效性和安全性。 GAMENet在所有有效性措施中的表现都超过了所有基线，并且从现有的EHR数据中也实现了3.60％的DDI率降低。
URL
https://arxiv.org/abs/1809.01852
