214 citations · 513 across the 28 of their papers we have counts for
12 papers · 1 filter
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
Ziyang Zhang, Hejie Cui, Ran Xu +3
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. Howe…
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
Hejie Cui, Xinyu Fang, Ran Xu +3
Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous informati…
R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan, Zimu Li, Hejie Cui +6
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological ent…
Transformer-Based Hierarchical Clustering for Brain Network Analysis
Wei Dai, Hejie Cui, Xuan Kan +3
Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain syst…
Neighborhood-Regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui +5
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been succ…
Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui +3
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transform…
Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning
Yi Yang, Yanqiao Zhu, Hejie Cui +4
Brain networks characterize complex connectivities among brain regions as graph structures, which provide a powerful means to study brain connectomes. In recent years, graph neural…
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Joshua Lukemire +2
Functional magnetic resonance imaging (fMRI) is one of the most common imaging modalities to investigate brain functions. Recent studies in neuroscience stress the great potential…
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Hejie Cui +3
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data. In this wor…
Effective and Interpretable fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Ying Guo +1
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing func…
BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu +3
Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience. GNNs are promising to model complicated network data, but they are…
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui, Zijie Lu, Pan Li +1
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where superior performance is mainly esta…