Making medication prescriptions in response to the patient's diagnosis is a challenging task.
The number of pharmaceutical companies, their inventory of medicines, and the …

Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a
few visits per patient and a few observations per disease can be exploited. Although meta …

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 …

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 …

With the wide application of electronic health records (EHR) in healthcare facilities, health
event prediction with deep learning has gained more and more attention. A common feature …

Exploiting the correlations between medical features is essential to the success of
healthcare data analysis. However, most existing methods are either suffering large …

Disease prediction based on patients' historical admission records is an essential task in the
medical field, but current predictive models often lack interpretability, which is a critical …

In recent years, deep learning has been successfully adopted in a wide range of
applications related to electronic health records (EHRs) such as representation learning and …

Clinical predictive models often rely on patients electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …

Electronic health records (EHRs) have been heavily used in modern healthcare systems for
recording patients' admission information to health facilities. Many data-driven approaches …