7 papers
Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
Feiqing Huang, Zongqi Xia, Rong Ma +1
We propose a spectral-based, unsupervised representation learning framework to derive low-dimensional embeddings for clinical concepts and patients in rare disease cohorts from ele…
Representation learning to advance multi-institutional studies with electronic health record data from US and France
Doudou Zhou, Han Tong, Linshanshan Wang +21
The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data acro…
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…
Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes
Mingyuan Xu, Zongqi Xia, Tianxi Cai +2
We often collect data from multiple sites (e.g., hospitals) that share common structure but also exhibit heterogeneity. This paper aims to learn robust sequential decision-making p…
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…
Latent Factor Point Processes for Patient Representation in Electronic Health Records
Parker Knight, Doudou Zhou, Zongqi Xia +2
Electronic health records (EHR) contain valuable longitudinal patient-level information, yet most statistical methods reduce the irregular timing of EHR codes into simple counts, t…