The medicine and healthcare sector has been evolving and advancing very fast. The
advancement has been initiated and shaped by the applications of data-driven, robust, and …

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 …

Today, despite decades of developments in medicine and the growing interest in precision
healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

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 …

Electronic medical records (EMRs) support the development of machine learning algorithms
for predicting disease incidence, patient response to treatment, and other healthcare events …

Suicide is a leading cause of death that defies prediction and challenges prevention efforts
worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means …

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

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

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