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 …

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

Abstract Machine Learning (ML) applications are making a considerable impact on
healthcare. ML is a subtype of Artificial Intelligence (AI) technology that aims to improve the …

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 …

Network intrusion detection systems (NIDSs) play a crucial role in defending computer
networks. However, there are concerns regarding the feasibility and sustainability of current …

Abstract Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML)
technology have brought on substantial strides in predicting and identifying health …

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 …

With a massive influx of multimodality data, the role of data analytics in health informatics
has grown rapidly in the last decade. This has also prompted increasing interests in the …

Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …

With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …