| 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 | 256 | 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 | 139 | 2017 |
| 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 | 96 | 2022 |
| 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 | 62 | 2019 |
| 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 | 56 | 2019 |
| 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 | 45 | 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 | 41 | 2020 |
| Classifying vaguely labeled data based on evidential fusion M Song, C Sun, D Cai, S Hong, H Li Information Sciences 583, 159-173, 2022 | 31 | 2022 |
| 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 | 28 | 2021 |
| Artificial-intelligence-enhanced mobile system for cardiovascular health management Z Fu, S Hong, R Zhang, S Du Sensors 21 (3), 773, 2021 | 28 | 2021 |
| 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 | 28* | 2020 |
| 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 | 27* | 2022 |
| 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 | 18 | 2019 |
| Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion L Yang, S Hong International Conference on Machine Learning, 25038-25054, 2022 | 16* | 2022 |
| 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 | 15 | 2017 |
| 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 | 13 | 2022 |
| 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 | 13 | 2020 |
| 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 | 12 | 2021 |
| Cardioid: learning to identification from electrocardiogram data S Hong, C Wang, Z Fu Neurocomputing 412, 11-18, 2020 | 12 | 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 | 11* | 2018 |
Junyuan ShangBaidu NLP在 pku.edu.cn 的电子邮件经过验证
