Graph representation learning is a method for introducing how to effectively construct and
learn patient embeddings using electronic medical records. Adapting the integration will …

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic
in both academia and industry. A recent study showed that utilizing the graphical structure …

Understanding the latent disease patterns embedded in electronic health records (EHRs) is
crucial for making precise and proactive healthcare decisions. Federated graph learning …

Electronic health records (EHRs) are longitudinal records of a patient's interactions with
healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top to …

Abstract Electronic Health Record (EHR) data is a rich source for powerful biomedical
discovery but it consists of a wide variety of data types that are traditionally difficult to model …

Electronic Health Records (EHR) are high-dimensional data with implicit connections
among thousands of medical concepts. These connections, for instance, the co-occurrence …

Various deep learning models have recently been applied to predictive modeling of
Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR …

With the wide application of electronic health records (EHR) in healthcare facilities, health
event prediction with deep learning has gained more and more attention. A common feature …

Computational prediction of in-hospital mortality in the setting of an intensive care unit can
help clinical practitioners to guide care and make early decisions for interventions. As …

With the rise of deep learning, several recent studies on deep learning-based methods for
electronic health records (EHR) successfully address real-world clinical challenges by …