Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and
applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda …

This paper targets at improving the generalizability of hypergraph neural networks in the low-
label regime, through applying the contrastive learning approach from images/graphs (we …

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 …

Abstract Electronic Health Record modeling is crucial for digital medicine. However, existing
models ignore higher-order interactions among medical codes and their causal relations …

Plenty of models have been presented to handle the hypergraph node classification.
However, very few of these methods consider contrastive learning, which is popular due to …

Group interactions arise in various scenarios in real-world systems: collaborations of
researchers, co-purchases of products, and discussions in online Q&A sites, to name a few …

We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …

Electronic health records (EHR) data contain rich information about patients' health
conditions including diagnosis, procedures, medications and etc., which have been widely …

Electronic Health Records (EHRs) have become increasingly popular to support clinical
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …

In this survey, we dive into Tabular Data Learning (TDL) using Graph Neural Networks
(GNNs), a domain where deep learning-based approaches have increasingly shown …