79 citations
- Georgia Institute of TechnologyUS5 papers
- University of Illinois Urbana-ChampaignUS5 papers
- Temple UniversityUS3 papers
- Peking UniversityCN2 papers
- University of MinnesotaUS2 papers
- University of Minnesota SystemUS2 papers
- University of VirginiaUS2 papers
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- IBM (United States)US1 paper
- Institute of SoftwareCN1 paper
- IQVIA (France)FR1 paper
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8 papers · 1 filter
Multi-version Tensor Completion for Time-delayed Spatio-temporal Data
Cheng Qian, Nikos Kargas, Cao Xiao +3
Real-world spatio-temporal data is often incomplete or inaccurate due to various data loading delays. For example, a location-disease-time tensor of case counts can have multiple d…
Change Matters: Medication Change Prediction with Recurrent Residual Networks
Chaoqi Yang, Cao Xiao, Lucas Glass +1
Deep learning is revolutionizing predictive healthcare, including recommending medications to patients with complex health conditions. Existing approaches focus on predicting all m…
STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization
Nikos Kargas, Cheng Qian, Nicholas D. Sidiropoulos +3
Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to…
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
Rakshith S Srinivasa, Cao Xiao, Lucas Glass +2
The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during b…
CLARA: Clinical Report Auto-completion
Siddharth Biswal, Cao Xiao, Lucas M. Glass +2
Generating clinical reports from raw recordings such as X-rays and electroencephalogram (EEG) is an essential and routine task for doctors. However, it is often time-consuming to w…
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
Junyi Gao, Cao Xiao, Yasha Wang +3
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Netw…