Publications by authors named "Jun-En Ding"
PARKINSON'S DISEASE CLASSIFICATION USING CONTRASTIVE GRAPH CROSS-VIEW LEARNING WITH MULTIMODAL FUSION OF SPECT IMAGES AND CLINICAL FEATURES.
Proc IEEE Int Symp Biomed Imaging
May 2024
Article Synopsis
- Parkinson's Disease (PD) is a global issue affecting movement, and previous research mainly focused on analyzing medical images without considering the underlying data structure.
- This study introduces a multimodal method that combines both image and clinical data, using advanced techniques like contrastive cross-view graph fusion for better PD classification.
- The approach achieves high accuracy (91%) and a strong AUC (92.8%) through improved feature extraction, demonstrating enhanced predictive power compared to traditional machine learning methods that only use images.
The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marijuana addiction, coupled with an analysis of localized brain network communities exhibiting abnormal activities among chronic marijuana users. HOGANN integrates dynamic intrinsic functional brain networks, estimated from functional magnetic resonance imaging (fMRI), using graph attentionbased long short-term memory (GAT-LSTM) to capture temporal network dynamics.
View Article and Find Full Text PDFType 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database.
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