34 citations · 63 across the 5 of their papers we have counts for
5 papers
Early GVHD Prediction in Liver Transplantation via Multi-Modal Deep Learning on Imbalanced EHR Data
Yushan Jiang, Shuteng Niu, Dongjin Song +5
Graft-versus-host disease (GVHD) is a rare but often fatal complication in liver transplantation, with a very high mortality rate. By harnessing multi-modal deep learning methods t…
Leveraging Vulnerabilities in Temporal Graph Neural Networks via Strategic High-Impact Assaults
Dong Hyun Jeon, Lijing Zhu, Haifang Li +6
Temporal Graph Neural Networks (TGNNs) have become indispensable for analyzing dynamic graphs in critical applications such as social networks, communication systems, and financial…
Launching Insights: A Pilot Study on Leveraging Real-World Observational Data from the Mayo Clinic Platform to Advance Clinical Research
Yue Yu, Xinyue Hu, Sivaraman Rajaganapathy +15
Backgrounds: Artificial intelligence (AI) is transforming healthcare, yet translating AI models from theoretical frameworks to real-world clinical applications remains challenging.…
Self-explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction
Xinyue Hu, Zenan Sun, Yi Nian +6
Background: Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While…
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
Yi Nian, Xinyue Hu, Rui Zhang +5
To date, there are no effective treatments for most neurodegenerative diseases. Knowledge graphs can provide comprehensive and semantic representation for heterogeneous data, and h…