activity
20172023
most citedSemi-supervised Rare Disease Detection Using Generative Adversarial Network

17 citations · 54 across the 9 of their papers we have counts for

collaborators

10 papers

cs.LG2023★ 4 cited

Towards Trustworthy Explanation: On Causal Rationalization

Wenbo Zhang, Tong Wu, Yunlong Wang +2

With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts…

cs.IR2022★ 6 cited

FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR

Luning Bi, Yunlong Wang, Fan Zhang +3

In the past decade, with the development of big data technology, an increasing amount of patient information has been stored as electronic health records (EHRs). Leveraging these d…

cs.LG2020

eTREE: Learning Tree-structured Embeddings

Faisal M. Almutairi, Yunlong Wang, Dong Wang +2

Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representati…

cs.LG2019★ 5 cited

Representation Learning of EHR Data via Graph-Based Medical Entity Embedding

Tong Wu, Yunlong Wang, Yue Wang +3

Automatic representation learning of key entities in electronic health record (EHR) data is a critical step for healthcare informatics that turns heterogeneous medical records into…

stat.AP2019★ 5 cited

Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning

Yue Wang, Tong Wu, Yunlong Wang +1

Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patient…

cs.LG2019★ 3 cited

Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model

Fan Zhang, Tong Wu, Yunlong Wang +5

Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the comm…