39 citations · 53 across the 8 of their papers we have counts for
8 papers
A SHAP-based explainable multi-level stacking ensemble learning method for predicting the length of stay in acute stroke
Zhenran Xu
Length of stay (LOS) prediction in acute stroke is critical for improving care planning. Existing machine learning models have shown suboptimal predictive performance, limited gene…
Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Yuan Xue, Denny Zhou, Nan Du +4
Capturing the inter-dependencies among multiple types of clinically-critical events is critical not only to accurate future event prediction, but also to better treatment planning.…
BEDS-Bench: Behavior of EHR-models under Distributional Shift--A Benchmark
Anand Avati, Martin Seneviratne, Emily Xue +3
Machine learning has recently demonstrated impressive progress in predictive accuracy across a wide array of tasks. Most ML approaches focus on generalization performance on unseen…
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records
Zhen Xu, David R. So, Andrew M. Dai
One important challenge of applying deep learning to electronic health records (EHR) is the complexity of their multimodal structure. EHR usually contains a mixture of structured (…
Deep Physiological State Space Model for Clinical Forecasting
Yuan Xue, Denny Zhou, Nan Du +4
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinic…
Flow Contrastive Estimation of Energy-Based Models
Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma +3
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial…
Learning an Adaptive Learning Rate Schedule
Zhen Xu, Andrew M. Dai, Jonas Kemp +1
The learning rate is one of the most important hyper-parameters for model training and generalization. However, current hand-designed parametric learning rate schedules offer limit…
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Edward Choi, Zhen Xu, Yujia Li +4
Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure…