Leveraging graph-based hierarchical medical entity embedding for healthcare applications

Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan
2021 Scientific Reports  
AbstractAutomatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare data mining that turns heterogeneous medical records into structured and actionable information. Here we propose , an algorithmic framework for learning continuous low-dimensional embedding vectors of the most common entities in EHR: medical services, doctors, and patients. features a hierarchical structure that encapsulates different node embedding schemes to cater
more » ... for the unique characteristic of each medical entity. To embed medical services, we employ a biased-random-walk-based node embedding that leverages the irregular time intervals of medical services in EHR to embody their relative importance. To embed doctors and patients, we adhere to the principle "it's what you do that defines you" and derive their embeddings based on their interactions with other types of entities through graph neural network and proximity-preserving network embedding, respectively. Using real-world clinical data, we demonstrate the efficacy of over competitive baselines on diagnosis prediction, readmission prediction, as well as recommending doctors to patients based on their medical conditions. In addition, medical service embeddings pretrained using can substantially improve the performance of sequential models in predicting patients clinical outcomes. Overall, can serve as a general-purpose representation learning algorithm for EHR data and benefit various downstream tasks in terms of both performance and interpretability.
doi:10.1038/s41598-021-85255-w pmid:33712670 pmcid:PMC7955058 fatcat:7acvfsmikzagvbjery2cm52vyq