With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

The recent series of innovations in deep learning (DL) have shown enormous potential to
impact individuals and society, both positively and negatively. DL models utilizing massive …

The explosive growth and widespread accessibility of medical information on the Internet
have led to a surge of research activity in a wide range of scientific communities including …

One important challenge of applying deep learning to electronic health records (EHR) is the
complexity of their multimodal structure. EHR usually contains a mixture of structured …

With the increasing availability of Electronic Health Records (EHRs) and advances in deep
learning techniques, developing deep predictive models that use EHR data to solve …

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative
and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph …

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-
diagnosis service for online users based on Electronic Healthcare Records (EHRs). Two …

Medication recommendation based on Electronic Health Records (EHRs) is an important
research direction, which aims to make prescription recommendations according to EHRs of …

An electronic health record (EHR) is a vital high-dimensional part of medical concepts.
Discovering implicit correlations in the information of this data set and the research and …