Leveraging graph-based hierarchical medical entity embedding for healthcare applications.

ABSTRACT: Automatic 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 ME2Vec, an algorithmic framework for learning continuous low-dimensional embedding vectors of the most common entities in EHR: medical services, doctors, and patients. ME2Vec features a hierarchical structure that encapsulates different node embedding schemes to cater 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 princi

SUBMITTER: Wu T 

PROVIDER: S-EPMC7955058 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature