MERGE: A Multi-graph Attentive Representation learning framework integrating Group information from similar patients.

It is an important research task in the field of medical big data to predict patient's future health status according to the historical temporal Electronic Health Records (EHRs). Most of the existing deep learning-based medical prediction methods only focus on the patient's individual information. However, due to the sparseness and low quality of EHR data, individual clinical records of single patient often cannot provide complete health information, which severely limits the accuracy of the prediction models. In this paper, we propose a Multi-graph attEntive Representation learning framework integrating Group information from similar patiEnts(MERGE) for medical prediction. In this framework, while capturing the individual patient's temporal characteristics through the individual representation learning module, the group representation leaning module is used to learn group representations of similar patients from different aspects as a supplement, thereby effectively improving the accuracy of patients' representation. We evaluate our method on the MIMIC-III dataset for the task of in-hospital mortality prediction and Xiangya dataset for cardiovascular diseases (CVDs) prediction. The experimental results show that MERGE outperforms the state-of-the-art methods.