89 citations · 244 across the 29 of their papers we have counts for
7 papers · 1 filter
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting
Zhihao Yu, Liantao Ma, Yasha Wang +1
Deep learning algorithms, especially Transformer-based models, have achieved significant performance by capturing long-range dependencies and historical information. However, the p…
Predict and Interpret Health Risk using EHR through Typical Patients
Zhihao Yu, Chaohe Zhang, Yasha Wang +3
Predicting health risks from electronic health records (EHR) is a topic of recent interest. Deep learning models have achieved success by modeling temporal and feature interaction.…
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets
Zhongji Zhang, Yuhang Wang, Yinghao Zhu +7
Emerging diseases present challenges in symptom recognition and timely clinical intervention due to limited available information. An effective prognostic model could assist physic…
PRISM: Mitigating EHR Data Sparsity via Learning from Missing Feature Calibrated Prototype Patient Representations
Yinghao Zhu, Zixiang Wang, Long He +4
Electronic Health Records (EHRs) contain a wealth of patient data; however, the sparsity of EHRs data often presents significant challenges for predictive modeling. Conventional im…
MFair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method
Yinghao Zhu, Jingkun An, Enshen Zhou +8
In the data-driven artificial intelligence paradigm, models heavily rely on large amounts of training data. However, factors like sampling distribution imbalance can lead to issues…
Fused Gromov-Wasserstein Graph Mixup for Graph-level Classifications
Xinyu Ma, Xu Chu, Yasha Wang +4
Graph data augmentation has shown superiority in enhancing generalizability and robustness of GNNs in graph-level classifications. However, existing methods primarily focus on the…
Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset
Liantao Ma, Chaohe Zhang, Junyi Gao +8
Objective: Peritoneal Dialysis (PD) is one of the most widely used life-supporting therapies for patients with End-Stage Renal Disease (ESRD). Predicting mortality risk and identif…