Patient Similarity via Medical Attributed Heterogeneous Graph Convolutional Network.

Electronic medical records (EMR) record the whole process of patients' diagnosis and treatment in the hospital, which contains a lot of valuable information. Through this information, medical services can be provided to patients in more timely and convenient manner. Accurately identifying patients with similar diseases based on EMR is the key to personalized healthcare. Most patient similarity studies mainly utilize discrete medical entities (drugs, procedures) embedded as patient feature representation. But these structured data could be either incomplete or erroneous which has a significant impact on the final patient representation. And the previous studies rarely considered the structural and semantic information existing between medical entities. Therefore, we propose a patient similarity framework based on a medical attributed heterogeneous graph convolution network, named AHGCN-PS. Firstly, the framework leverages the patients' medical entity and incorporates the patients' medical text as the attributes of patients to obtain more integral patient information. Then, we construct a medical attributed heterogeneous information network from EMR, capturing the structural information in the network and the hidden semantic information between different nodes by selecting different meta-paths. Then, we adopt a graph convolutional neural network and a semantic attention mechanism to aggregate node neighbor information and meta-path semantic information. Finally, this paper uses the obtained patient node feature representation for patient similarity calculation. We use the real-world ICU patient dataset MIMIC-III to evaluate the experimental performance of AHGCN-PS, the experimental results demonstrate the effectiveness and feasibility of the patient similarity framework. [ABSTRACT FROM AUTHOR]