MDP-GRL: Multi-disease Prediction by Graph-enabled Representation Learning

In recent years, automatic disease prediction based on electronic health records (EHRs) has emerged as a focal area of research in medical informatics. While successfully facilitating disease diagnosis, this technique still suffers from many limitations caused by the complexity of medical data, particularly the diverse relations and shared risk factors among multiple diseases. Besides, the data sparsity and imbalanced problem in EHR also undermines the effectiveness of existing approaches. Therefore, new approaches are urgently needed to accommodate the EHR features better and make effective predictions on individuals' potential diseases. To address the above challenges, this paper proposes MDP-GRL, a novel multi-label disease prediction model based on graph-enabled representation learning. Specifically, MDP-GRL constructs a medical knowledge graph (MKG) based on the patient and disease information in EHR and then employs a graph neural network (GNN) to realise the disease prediction. To address the data sparsity issue, it incorporates supplementary data for both patients and diseases, i.e., enriching patient nodes by personal basic information, examination indicators, and illness history, and supplementing disease information with comorbidity information, prevalent populations, common causes, and diagnostic basis. To mitigate the data complexity issue, MDP-GRL considers four different relation patterns in MKG, which optimizes the modelling capabilities. To address the data imbalance problem, it introduces an attention mechanism and self-adversarial negative sampling strategy, which further enhance MDP-GRL's ability to identify error-prone and minority samples. Comprehensive experiments and ablation studies are conducted based on the MIMIC-IV dataset. The results demonstrate MDP-GRL's superiority in multi-disease prediction compared with state-of-the-art approaches.