Similarity calculation model between patients with Chinese electronic medical records based on multi-view hierarchical learning network

The inherent sparsity of electronic medical record (EMR) poses difficulties for the learning of patient similarity. Graph-based modeling methods can infer missing values by learning complex relations among medical facts, which are becoming the mainstream options for patient similarity analysis. However, existing graph-based solutions mainly focus on general patterns among patients and overlook local differences, potentially losing semantic information on patient similarity. Additionally, in real medical situations, entities related to symptoms or treatment types often involve multiple diseases, which provide erroneous signals in similarity assessment. Therefore, this paper proposes a novel deep learning method called Multi-view Hierarchical Learning Network (MHLN) for patient similarity measurement. This method extracts dependency information between entities from both local and global perspectives. Additionally, it assigns different importance levels to different types of medical entities, enabling the learning of dependency features between entity representations through local and global encoders to generate representative embeddings for similarity computation. Finally, we evaluate MHLN on real-world Chinese EMR data, and the results demonstrate the effectiveness of MHLN compared to related work.