Deep learning models have been widely used to assist doctors with clinical decision-
making. However, these models often encounter a significant performance drop when …

Synthetic electronic health records (EHRs) that are both realistic and privacy-preserving
offer alternatives to real EHRs for machine learning (ML) and statistical analysis. However …

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

The vast amount of health data has been continuously collected for each patient, providing
opportunities to support diverse healthcare predictive tasks such as seizure detection and …

Medical applications of machine learning (ML) have experienced a surge in popularity in
recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of …

Electroencephalography (EEG) is a non-invasive technique to measure and record brain
electrical activity, widely used in various BCI and healthcare applications. Early EEG …

Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated
class probabilities. In high-risk applications like healthcare, practitioners require $\textit {fully …

The drug recommendation task aims to predict safe and effective drug prescriptions based
on the patients' historical electronic health records (EHRs). However, existing drug …

Drug recommendation that aims to provide a prescription for a patient is an essential task in
healthcare. Drug molecular graphs provide valuable support for drug recommendation …

Biological signals, such as electroencephalograms (EEG), play a crucial role in numerous
clinical applications, exhibiting diverse data formats and quality profiles. Current deep …