Shenda Hong
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Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review
S Hong, Y Zhou, J Shang, C Xiao, J Sun
Computers in Biology and Medicine, 103801, 2020
ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks
S Hong, M Wu, Y Zhou, Q Wang, J Shang, H Li, J Xie
2017 Computing in cardiology (cinc), 1-4, 2017
Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings
S Hong, Y Zhou, M Wu, J Shang, Q Wang, H Li, J Xie
Physiological measurement 40 (5), 054009, 2019
MINA: multilevel knowledge-guided attention for modeling electrocardiography signals
S Hong, C Xiao, T Ma, H Li, J Sun
International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2019
Diffusion models: A comprehensive survey of methods and applications
L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao, Y Shao, W Zhang, B Cui, ...
arXiv preprint arXiv:2209.00796, 2022
Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
C Sun, S Hong, M Song, H Li, Z Wang
BMC Medical Informatics and Decision Making 21 (1), 1-16, 2021
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
S Hong, Y Xu, A Khare, S Priambada, K Maher, A Aljiffry, J Sun, ...
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
G Spadon, S Hong, B Brandoli, S Matwin, JF Rodrigues-Jr, J Sun
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
A Systematic Review of Echo State Networks from Design to Application
C Sun, M Song, D Cai, B Zhang, S Hong, H Li
IEEE Transactions on Artificial Intelligence, 2022
Classifying vaguely labeled data based on evidential fusion
M Song, C Sun, D Cai, S Hong, H Li
Information Sciences 583, 159-173, 2022
A review of deep learning methods for irregularly sampled medical time series data
C Sun, S Hong, M Song, H Li
arXiv preprint arXiv:2010.12493, 2020
Artificial-intelligence-enhanced mobile system for cardiovascular health management
Z Fu, S Hong, R Zhang, S Du
Sensors 21 (3), 773, 2021
K-margin-based Residual-Convolution-Recurrent Neural Network for Atrial Fibrillation Detection
Y Zhou, S Hong, J Shang, M Wu, Q Wang, H Li, J Xie
International Joint Conference on Artificial Intelligence (IJCAI) 2019, 2019
Event2vec: Learning representations of events on temporal sequences
S Hong, M Wu, H Li, Z Wu
Web and Big Data: First International Joint Conference, APWeb-WAIM 2017 …, 2017
Deep active learning for interictal ictal injury continuum EEG patterns
W Ge, J Jing, S An, A Herlopian, M Ng, AF Struck, B Appavu, EL Johnson, ...
Journal of neuroscience methods 351, 108966, 2021
Cardioid: learning to identification from electrocardiogram data
S Hong, C Wang, Z Fu
Neurocomputing 412, 11-18, 2020
Cardiolearn: a cloud deep learning service for cardiac disease detection from electrocardiogram
S Hong, Z Fu, R Zhou, J Yu, Y Li, K Wang, G Cheng
Companion Proceedings of the Web Conference 2020, 148-152, 2020
Knowledge guided multi-instance multi-label learning via neural networks in medicines prediction
J Shang, S Hong, Y Zhou, M Wu, H Li
Asian Conference on Machine Learning, 831-846, 2018
Intra-inter subject self-supervised learning for multivariate cardiac signals
X Lan, D Ng, S Hong, M Feng
Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4532-4540, 2022
Addressing noise and skewness in interpretable health-condition assessment by learning model confidence
Y Zhou, S Hong, J Shang, M Wu, Q Wang, H Li, J Xie
Sensors 20 (24), 7307, 2020
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