| RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism E Choi, MT Bahadori, J Sun, J Kulas, A Schuetz, W Stewart Advances in Neural Information Processing Systems, 3504-3512, 2016 | 1592 | 2016 |
| Doctor ai: Predicting clinical events via recurrent neural networks E Choi, MT Bahadori, A Schuetz, WF Stewart, J Sun Machine Learning for Healthcare Conference, 301-318, 2016 | 1527 | 2016 |
| Social influence analysis in large-scale networks J Tang, J Sun, C Wang, Z Yang Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 1337 | 2009 |
| Using recurrent neural network models for early detection of heart failure onset E Choi, A Schuetz, WF Stewart, J Sun Journal of the American Medical Informatics Association 24 (2), 361-370, 2017 | 1029 | 2017 |
| Generating multi-label discrete patient records using generative adversarial networks E Choi, S Biswal, B Malin, J Duke, WF Stewart, J Sun Machine learning for healthcare conference, 286-305, 2017 | 850 | 2017 |
| Generating multi-label discrete patient records using generative adversarial networks E Choi, S Biswal, B Malin, J Duke, WF Stewart, J Sun Machine learning for healthcare conference, 286-305, 2017 | 850 | 2017 |
| Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review C Xiao, E Choi, J Sun Journal of the American Medical Informatics Association 25 (10), 1419-1428, 2018 | 800 | 2018 |
| The TPR*-tree: an optimized spatio-temporal access method for predictive queries Y Tao, D Papadias, J Sun Proceedings of the 29th international conference on Very large data bases …, 2003 | 786 | 2003 |
| GRAM: Graph-based Attention Model for Healthcare Representation Learning E Choi, MT Bahadori, L Song, WF Stewart, J Sun Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017 | 785 | 2017 |
| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 716 | 2023 |
| Explainable Prediction of Medical Codes from Clinical Text J Mullenbach, S Wiegreffe, J Duke, J Sun, J Eisenstein arXiv preprint arXiv:1802.05695, 2018 | 710 | 2018 |
| GraphScope: parameter-free mining of large time-evolving graphs J Sun, C Faloutsos, S Papadimitriou, PS Yu Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007 | 677 | 2007 |
| Beyond streams and graphs: dynamic tensor analysis J Sun, D Tao, C Faloutsos Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 650 | 2006 |
| Multi-layer representation learning for medical concepts E Choi, MT Bahadori, E Searles, C Coffey, M Thompson, J Bost, ... Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 632 | 2016 |
| Streaming pattern discovery in multiple time-series S Papadimitriou, J Sun, C Faloutsos Proceedings of the 31st international conference on Very large data bases …, 2005 | 579 | 2005 |
| Temporal recommendation on graphs via long-and short-term preference fusion L Xiang, Q Yuan, S Zhao, L Chen, X Zhang, Q Yang, J Sun Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010 | 535 | 2010 |
| Neighborhood formation and anomaly detection in bipartite graphs J Sun, H Qu, D Chakrabarti, C Faloutsos Fifth IEEE International Conference on Data Mining (ICDM'05), 8 pp., 2005 | 485 | 2005 |
| Scalable tensor decompositions for multi-aspect data mining TG Kolda, J Sun 2008 Eighth IEEE international conference on data mining, 363-372, 2008 | 483 | 2008 |
| 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 122, 103801, 2020 | 449 | 2020 |
| From hype to reality: data science enabling personalized medicine H Fröhlich, R Balling, N Beerenwinkel, O Kohlbacher, S Kumar, ... BMC medicine 16, 1-15, 2018 | 439 | 2018 |
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