Publications

2023

1. Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models

   Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce Ho, and Carl Yang

   arXiv preprint arXiv:2311.00287, 2023.

   arXiv Code
2. Weakly-supervised Scientific Document Classification via Retrieval-Augmented Multi-stage Training

   Ran Xu*, Yue Yu*, Joyce C Ho, and Carl Yang

   Proceedings of SIGIR, 2023.

   arXiv Code
3. Neighborhood-regularized Self-Training for Learning with Few Labels

   Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce Ho, Chao Zhang, and Carl Yang

   Proceedings of AAAI, 2023. (Oral)

   arXiv PDF Code
4. Hypergraph Transformers for EHR-based Clinical Predictions

   Ran Xu, Mohammed K Ali, Joyce C Ho, and Carl Yang

   AMIA Summits on Translational Science Proceedings, 2023. (Oral)

   PDF
5. Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting

   Hejie Cui, Xinyu Fang, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Yue Yu, Manling Li, Yangqiu Song, and Carl Yang

   Proceedings of NeurIPS, 2023.

   arXiv PDF
6. Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach

   Yue Yu, Rongzhi Zhang, Ran Xu, Jieyu Zhang, Jiaming Shen, and Chao Zhang

   Proceedings of ACL, 2023.

   Abs PDF Code

   We present PATRON, a prompt-based data selection method for pre-trained language model fine-tuning under cold-start scenarios, i.e., no initial labeled data are available. In PATRON, we design (1) a prompt-based uncertainty propagation approach to estimate the importance of data points and (2) a partition-then-rewrite (PTR) strategy to promote sample diversity when querying for annotations. Experiments on six text classification datasets show that PATRON outperforms the strongest cold-start data selection baselines by up to 6.9%. Besides, with 128 labels only, PATRON achieves 91.0% and 92.1% of the fully supervised performance based on vanilla fine-tuning and prompt-based learning respectively. Our implementation of PATRON will be published upon acceptance.

7. R-Mixup: Riemannian Mixup for Biological Networks

   Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, Ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, and Carl Yang

   Proceedings of KDD, 2023.

   arXiv PDF
8. Deep DAG Learning on Brain Networks for fMRI Analysis

   Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, and Carl Yang

   Proceedings of ISBI, 2023.

   arXiv PDF
9. A Survey on Knowledge Graphs for Healthcare: Resources, Application Progress, and Promise

   Hejie Cui, Jiaying Lu, Shiyu Wang, Ran Xu, Wenjing Ma, Shaojun Yu, Yue Yu, Xuan Kan, Tianfan Fu, Chen Ling, Joyce Ho, Fei Wang, and Carl Yang

   Proceedings of ICML (IMLH Workshop), 2023.

   arXiv PDF

2022

1. Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR

   Ran Xu, Yue Yu, Chao Zhang, Mohammed K Ali, Joyce C Ho, and Carl Yang

   Proceedings of ML4H, 2022. (Best Paper Award)

   Abs PDF Code

   Electronic Health Record modeling is crucial for digital medicine. However, existing models ignore higher-order interactions among medical codes and their causal relations towards downstream clinical predictions. To address such limitations, we propose a novel framework CACHE, to provide effective and insightful clinical predictions based on hypergraph representation learning and counterfactual and factual reasoning techniques. Experiments on two real EHR datasets show the superior performance of CACHE. Case studies with a domain expert illustrate a preferred capability of CACHE in generating clinically meaningful interpretations towards the correct predictions.