3 citations · 3 across the 8 of their papers we have counts for
10 papers
Incentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction
Pengfei Hu, Chenwei Wu, Qiao Jin +4
Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction. However, most…
Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning
Xiaoxue Han, Libo Zhang, Zining Zhu +1
We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each c…
Exploring Accurate and Transparent Domain Adaptation in Predictive Healthcare via Concept-Grounded Orthogonal Inference
Pengfei Hu, Chang Lu, Feifan Liu +1
Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While d…
HydroDCM: Hydrological Domain-Conditioned Modulation for Cross-Reservoir Inflow Prediction
Pengfei Hu, Fan Ming, Xiaoxue Han +3
Deep learning models have shown promise in reservoir inflow prediction, yet their performance often deteriorates when applied to different reservoirs due to distributional differen…
Adaptive Graph Learning with Transformer for Multi-Reservoir Inflow Prediction
Pengfei Hu, Ming Fan, Xiaoxue Han +5
Reservoir inflow prediction is crucial for water resource management, yet existing approaches mainly focus on single-reservoir models that ignore spatial dependencies among interco…
Discovering Hierarchy-Grounded Domains with Adaptive Granularity for Clinical Domain Generalization
Pengfei Hu, Xiaoxue Han, Fei Wang +1
Domain generalization has become a critical challenge in predictive healthcare, where different patient groups exhibit shifting data distributions that degrade model performance. S…
No Black Boxes: Interpretable and Interactable Predictive Healthcare with Knowledge-Enhanced Agentic Causal Discovery
Xiaoxue Han, Pengfei Hu, Jun-En Ding +3
Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in d…
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Records
Eric Yang, Pengfei Hu, Xiaoxue Han +1
The adoption of digital systems in healthcare has resulted in the accumulation of vast electronic health records (EHRs), offering valuable data for machine learning methods to pred…
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
Xiaoxue Han, Huzefa Rangwala, Yue Ning
Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the general…
A Topology-aware Graph Coarsening Framework for Continual Graph Learning
Xiaoxue Han, Zhuo Feng, Yue Ning
Continual learning on graphs tackles the problem of training a graph neural network (GNN) where graph data arrive in a streaming fashion and the model tends to forget knowledge fro…