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Zhihao Y, Xu C, Yujie J, Yasha W, Junfeng Z. Predict and Interpret Health Risk Using Ehr Through Typical Patients, in Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024).; 2024.

Xu Yongxin, Jiang Xinke, Xu， C, Yuzhen， X, Zhang Chaohe, Ding Hongxin, Junfeng Z, Yasha W, Bing X. ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup, in the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024).; 2024.

Gao X, Lin Y, Li R, Wang Y, Chu X, Ma X, Yu H. Enhancing Topic Interpretability for Neural Topic Modeling Through Topic-Wise Contrastive Learning, in 2024 IEEE 40th International Conference on Data Engineering (ICDE 2024).; 2024.

Yu Z, Zhang C, Wang Y, Tang W, Wang J, Ma L. Predict and Interpret Health Risk Using Ehr Through Typical Patients, in ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024).; 2024.

Jiang X, Qiu R, Xu Y, Zhang W, Zhu Y, Zhang R, Fang Y, Chu X, Zhao J, Wang Y. RAGraph: A General Retrieval-Augmented Graph Learning Framework. Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024). 2024.

Ma X, Chu X, Wang Y, Lin Y, Zhao J, Ma L, Zhu W. Fused gromov-wasserstein graph mixup for graph-level classifications. Advances in Neural Information Processing Systems. 2024;36.

Yang K, Xu Y, Zou P, Ding H, Zhao J, Wang Y, Xie B. KerPrint: local-global knowledge graph enhanced diagnosis prediction for retrospective and prospective interpretations, in Proceedings of the AAAI Conference on Artificial Intelligence.Vol 37.; 2023:5357–5365.

Ma L, Zhang C, Gao J, Jiao X, Yu Z, Zhu Y, Wang T, Ma X, Wang Y, Tang W, et al. Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. Patterns. 2023;4.

Xu Y, Chu X, Yang K, Wang Z, Zou P, Ding H, Zhao J, Wang Y, Xie B. Seqcare: Sequential training with external medical knowledge graph for diagnosis prediction in healthcare data, in Proceedings of the ACM Web Conference 2023.; 2023:2819–2830.

Chu X, Jin Y, Wang X, Zhang S, Wang Y, Zhu W, Mei H. Wasserstein barycenter matching for graph size generalization of message passing neural networks, in International Conference on Machine Learning. PMLR; 2023:6158–6184.

Chu X, Jin Y, Zhu W, Wang Y, Wang X, Zhang S, Mei H. Dna: Domain generalization with diversified neural averaging, in International conference on machine learning. PMLR; 2022:4010–4034.

Jin Y, Chu X, Wang Y, Zhu W. Domain generalization through the lens of angular invariance. arXiv preprint arXiv:2210.15836. 2022.

Zhang C, Chu X, Ma L, Zhu Y, Wang Y, Wang J, Zhao J. M3care: Learning with missing modalities in multimodal healthcare data, in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.; 2022:2418–2428.

Ma X, Wang Y, Chu X, Ma L, Tang W, Zhao J, Yuan Y, Wang G. Patient health representation learning via correlational sparse prior of medical features. IEEE Transactions on Knowledge and Data Engineering. 2022;35:11769–11783.

Feng Y, Wang J, Wang Y, Helal S. Completing missing prevalence rates for multiple chronic diseases by jointly leveraging both intra-and inter-disease population health data correlations, in Proceedings of the Web Conference 2021.; 2021:183–193.

Ma L, Ma X, Gao J, Jiao X, Yu Z, Zhang C, Ruan W, Wang Y, Tang W, Wang J. Distilling knowledge from publicly available online EMR data to emerging epidemic for prognosis, in Proceedings of the Web Conference 2021.; 2021:3558–3568.

Zhang C, Gao X, Ma L, Wang Y, Wang J, Tang W. GRASP: generic framework for health status representation learning based on incorporating knowledge from similar patients, in Proceedings of the AAAI conference on artificial intelligence.Vol 35.; 2021:715–723.

Wu J, Ma J, Wang Y, Wang J. Understanding and predicting the burst of burnout via social media. Proceedings of the ACM on Human-Computer Interaction. 2021;4:1–27.

Yang K, Liu S, Zhao J, Wang Y, Xie B. COTSAE: co-training of structure and attribute embeddings for entity alignment, in Proceedings of the AAAI Conference on Artificial Intelligence.Vol 34.; 2020:3025–3032.