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20182020
most citedRare Disease Detection by Sequence Modeling with Generative Adversarial Networks

13 citations · 21 across the 3 of their papers we have counts for

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Showing cs.LGShow all

6 papers · 1 filter

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.LG20195 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…

cs.LG20193 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…

cs.LG201913 cited

Rare Disease Detection by Sequence Modeling with Generative Adversarial Networks

Kezi Yu, Yunlong Wang, Yong Cai +4

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an im…

cs.LG2018

Modeling Treatment Delays for Patients using Feature Label Pairs in a Time Series

Weiyu Huang, Yunlong Wang, Li Zhou +3

Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of…

cs.LG2018

Semi-supervised Rare Disease Detection Using Generative Adversarial Network

Wenyuan Li, Yunlong Wang, Yong Cai +3

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is…