Objective: This study aims to comprehensively review the use of graph neural networks
(GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary …

While recent developments of deep learning models have led to record-breaking
achievements in many areas, the lack of sufficient interpretation remains a problem for many …

Recent advancements in AI applications to healthcare have shown incredible promise in
surpassing human performance in diagnosis and disease prognosis. With the increasing …

Medication combination prediction (MCP) can provide assistance for experts in the more
thorough comprehension of complex mechanisms behind health and disease. Many recent …

For care of chronic diseases (eg, depression, diabetes, hypertension), it is critical to identify
effective treatment pathways that aim to promptly update the medication following the …

Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis
prediction scenarios, most works are devoted to learning powerful patient representations …

Multivariate time series (MTS) forecasting penetrates various aspects of our economy and
society, whose roles become increasingly recognized. However, often MTS forecasting is …

Electronic health records (EHRs) contain vast medical information like diagnosis,
medication, and procedures, enabling personalized drug recommendations and treatment …

How to handle time features shall be the core question of any time series forecasting model.
Ironically, it is often ignored or misunderstood by deep-learning based models, even those …

The use of electronic health records has become widespread, providing a valuable source
of information for predicting disease risk. While deep neural network models have been …