Big data technologies are increasingly used for biomedical and health-care informatics
research. Large amounts of biological and clinical data have been generated and collected …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …

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

Deriving disease subtypes from electronic health records (EHRs) can guide next-generation
personalized medicine. However, challenges in summarizing and representing patient data …

With the widespread adoption of electronic health records (EHRs), large repositories of
structured and unstructured patient data are becoming available to conduct observational …

Objective: To perform a review of recent research in clinical data reuse or secondary use,
and envision future advances in this field. Methods: The review is based on a large literature …

An important goal of the health system is to identify new adverse drug events (ADEs) in the
postapproval period. Data‐mining methods that can transform data into meaningful …

Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of
the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) …

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