214 citations · 513 across the 28 of their papers we have counts for
29 papers
KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs
Yuzhang Xie, Hejie Cui, Ziyang Zhang +5
Medical diagnosis prediction plays a critical role in disease detection and personalized healthcare. While machine learning (ML) models have been widely adopted for this task, thei…
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Hejie Cui, Lingjun Mao, Xin Liang +5
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these founda…
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…
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Hejie Cui, Zhuocheng Shen, Jieyu Zhang +4
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning m…
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…
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Ran Xu, Hejie Cui, Yue Yu +6
Clinical natural language processing requires methods that can address domain-specific challenges, such as complex medical terminology and clinical contexts. Recently, large langua…
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui, Xinyu Fang, Zihan Zhang +7
Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., su…
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis
Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui +2
Recent neuroimaging studies have highlighted the importance of network-centric brain analysis, particularly with functional magnetic resonance imaging. The emergence of Deep Neural…
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…
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Hejie Cui, Jiaying Lu, Ran Xu +14
This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications…
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui, Rongmei Lin, Nasser Zalmout +4
Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extr…
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
Yi Yang, Hejie Cui, Carl Yang
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have…
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…
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
Chen Ling, Xujiang Zhao, Jiaying Lu +21
Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of app…
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…
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
Yue Yu, Xuan Kan, Hejie Cui +9
Functional magnetic resonance imaging (fMRI) has become one of the most common imaging modalities for brain function analysis. Recently, graph neural networks (GNN) have been adopt…
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…
Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu +3
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional…
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…
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu +7
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neur…
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Hejie Cui, Jiaying Lu, Yao Ge +1
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured…
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…