GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

By Junyuan Shang, Cao Xiao, Tengfei Ma, Hongyan Li, Jimeng Sun. AAAI Conference on Artificial Intelligence, 2018

365 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e569131d813be4ed3b027162acf6f130301d3172

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jimeng Sun
2. Cao Xiao
3. Tengfei Ma
4. Junyuan Shang
5. Edward Choi
6. Bolin Ding
7. Zhewei Wei
8. Yaliang Li

In Computational Theory and Mathematics

1. Jimeng Sun
2. Cao Xiao
3. Bolin Ding
4. Tengfei Ma
5. Yujia Li

In Molecular Biology

1. Jimeng Sun
2. Edward Choi
3. Cao Xiao
4. Zhewei Wei
5. Tengfei Ma
6. Junyuan Shang
7. Yujia Li

Citations in the corpus

• Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer (2019, methodology): “There are recent works that use Transformer on medical records (Song et al. 2018; Wang and Xu 2019; Shang et al. 2019a; Li et al. 2019), but they either simply replace RNN with Transformer to handle ICU records, or directly apply BERT learning objective on medical records, and do not utilize the…”
• Simple and Deep Graph Convolutional Networks (2020, background): “…range of applications, including social analysis (Qiu et al., 2018; Li & Goldwasser, 2019), traffic prediction (Guo et al., 2019; Li et al., 2019), biology (Fout et al., 2017; Shang et al., 2019), recommender systems (Ying et al., 2018), and computer vision (Zhao et al., 2019; Ma et al., 2019).”
• Pre-training of Graph Augmented Transformers for Medication Recommendation (2019, methodology): “GAMENet [Shang et al., 2019] is the method to recommend accuracy and safe medication based on memory neural networks and graph convolutional networks by leveraging EHR data and Drug-Drug Interaction (DDI) data source.”