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

Artificial intelligence (AI) is making computer systems capable of executing human brain
tasks in many fields in all aspects of daily life. The enhancement in information and …

Personalized predictive medicine necessitates the modeling of patient illness and care
processes, which inherently have long-term temporal dependencies. Healthcare …

There is increased interest in deploying big data technology in the healthcare industry to
manage massive collections of heterogeneous health datasets such as electronic health …

Background Data mining of electronic health records (EHRs) has a huge potential for
improving clinical decision support and to help healthcare deliver precision medicine …

A fundamental obstacle to healthcare transformation continues to be the acquisition of
knowledge and insightful data from complex, high dimensional, and heterogeneous …

Background and objective Alzheimer's disease (AD) is the most common type of dementia
that can seriously affect a person's ability to perform daily activities. Estimates indicate that …

The use of a stratified psychiatry approach that combines electronic health records (EHR)
data with machine learning (ML) is one potentially fruitful path toward rapidly improving …

Objectives: This survey aims at reviewing the literature related to Clinical Information
Systems (CIS), Hospital Information Systems (HIS), Electronic Health Record (EHR) …

Deep neural networks must address the dual challenge of delivering high-accuracy
predictions and providing user-friendly explanations. While deep models are widely used in …