4 papers
Knowledge-Embedded Latent Projection for Robust Representation Learning
Weijing Tang, Ming Yuan, Zongqi Xia +1
Latent space models are widely used for analyzing high-dimensional discrete data matrices, such as patient-feature matrices in electronic health records (EHRs), by capturing comple…
DANIEL: A Distributed and Scalable Approach for Global Representation Learning with EHR Applications
Zebin Wang, Ziming Gan, Weijing Tang +4
Classical probabilistic graphical models face fundamental challenges in modern data environments, which are characterized by high dimensionality, source heterogeneity, and stringen…
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition
Zebin Wang, Menghan Lin, Bolin Shen +4
Graph Neural Networks (GNNs) have demonstrated remarkable utility across diverse applications, and their growing complexity has made Machine Learning as a Service (MLaaS) a viable…
Semi-supervised Clustering Through Representation Learning of Large-scale EHR Data
Linshanshan Wang, Mengyan Li, Zongqi Xia +2
Electronic Health Records (EHR) offer rich real-world data for personalized medicine, providing insights into disease progression, treatment responses, and patient outcomes. Howeve…