In the last few years, the application of Machine Learning approaches like Deep Neural
Network (DNN) models have become more attractive in the healthcare system given the …

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 …

In a short period of time, many areas of science have made a sharp transition towards data-
dependent methods. In some cases, this process has been enabled by simultaneous …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

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 …

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 …

Deep learning methods exhibit promising performance for predictive modeling in healthcare,
but two important challenges remain:-Data insufficiency: Often in healthcare predictive …

Predicting the future health information of patients from the historical Electronic Health
Records (EHR) is a core research task in the development of personalized healthcare …