7 citations · 12 across the 16 of their papers we have counts for
18 papers
DIASENTINEL: An Auditable Multi-Agent System for Guideline-Grounded Diabetes Risk Screening
Yung Wei Shueh, Zhi-Jie Chen, Chia-Hsuan Hsu +9
Large language models (LLMs) offer promising clinical decision support but remain vulnerable to hallucinated facts, unsupported recommendations, and citation errors. We present DIA…
NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning
Guoan Wang, Shihao Yang, Jun-en Ding +2
Electroencephalography (EEG) provides a non-invasive window into neural dynamics at high temporal resolution and plays a pivotal role in clinical neuroscience research. Despite thi…
Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis
Tongze Zhang, Jun-En Ding, Melik Ozolcer +5
Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physic…
NeuroWeaver: An Autonomous Evolutionary Agent for Exploring the Programmatic Space of EEG Analysis Pipelines
Guoan Wang, Shihao Yang, Feng Liu
Although foundation models have achieved remarkable success in general domains, applying them to electroencephalography (EEG) analysis is constrained by substantial data requiremen…
CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction
Cong-Tinh Dao, Nguyen Minh Thao Phan, Jun-En Ding +12
Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians…
Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review
Shihao Yang, Xiying Huang, Danilo Bernardo +10
The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In t…
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Jun-En Ding, Anna Zilverstand, Shihao Yang +2
Dementia disorders such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) exhibit overlapping electrophysiological signatures in electroencephalography (EEG) that chall…
RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning
Chia-Hsuan Hsu, Jun-En Ding, Hsin-Ling Hsu +5
Medical question answering requires advanced reasoning that integrates domain knowledge with logical inference. However, existing large language models (LLMs) often generate reason…
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery
Xiaoxue Han, Pengfei Hu, Jun-En Ding +3
Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in d…
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Jing Wang, Jun-En Ding, Feng Liu +4
Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reasoning ability, and behavior, espec…
MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang +12
Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We id…
NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders
Jun-En Ding, Dongsheng Luo, Anna Zilverstand +2
Mental disorders are among the most widespread diseases globally. Analyzing functional brain networks through functional magnetic resonance imaging (fMRI) is crucial for understand…
Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning
Jun-En Ding, Chien-Chin Hsu, Chi-Hsiang Chu +2
The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimod…
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Thao Minh Nguyen Phan, Cong-Tinh Dao, Chenwei Wu +7
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice,…
EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models
Chun-Chieh Liao, Wei-Ting Kuo, I-Hsuan Hu +4
Traditional diagnosis of chronic diseases involves in-person consultations with physicians to identify the disease. However, there is a lack of research focused on predicting and d…
Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data
Jun-En Ding, Phan Nguyen Minh Thao, Wen-Chih Peng +10
Chronic diseases such as diabetes are the leading causes of morbidity and mortality worldwide. Numerous research studies have been attempted with various deep learning models in di…
Spatial Craving Patterns in Marijuana Users: Insights from fMRI Brain Connectivity Analysis with High-Order Graph Attention Neural Networks
Jun-En Ding, Shihao Yang, Anna Zilverstand +3
The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order gr…
Parkinson's Disease Classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features
Jun-En Ding, Chien-Chin Hsu, Feng Liu
Parkinson's Disease (PD) affects millions globally, impacting movement. Prior research utilized deep learning for PD prediction, primarily focusing on medical images, neglecting th…