Objective To conduct a systematic review of deep learning models for electronic health
record (EHR) data, and illustrate various deep learning architectures for analyzing different …

The past decade has seen an explosion in the amount of digital information stored in
electronic health records (EHRs). While primarily designed for archiving patient information …

Despite the recent developments in deep learning models, their applications in clinical
decision-support systems have been very limited. Recent digitalisation of health records …

Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

Abstract Introduction: Electronic Health Record (EHR) is a significant source of medical data
that can be used to develop predictive modelling with therapeutically useful outcomes …

Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …

The wide implementation of electronic health record (EHR) systems facilitates the collection
of large-scale health data from real clinical settings. Despite the significant increase in …

Objectives: Implementation of Electronic Health Record (EHR) systems continues to expand.
The massive number of patient encounters results in high amounts of stored data …