| Classifying heart sound recordings using deep convolutional neural networks and mel-frequency cepstral coefficients J Rubin, R Abreu, A Ganguli, S Nelaturi, I Matei, K Sricharan 2016 Computing in cardiology conference (CinC), 813-816, 2016 | 183 | 2016 |
| Computer poker: A review J Rubin, I Watson Artificial intelligence 175 (5-6), 958-987, 2011 | 168 | 2011 |
| A wide and deep transformer neural network for 12-lead ECG classification A Natarajan, Y Chang, S Mariani, A Rahman, G Boverman, S Vij, J Rubin 2020 Computing in Cardiology, 1-4, 2020 | 141 | 2020 |
| Cardiac arrhythmia detection using deep learning: A review S Parvaneh, J Rubin, S Babaeizadeh, M Xu-Wilson Journal of electrocardiology 57, S70-S74, 2019 | 138 | 2019 |
| Recognizing abnormal heart sounds using deep learning J Rubin, R Abreu, A Ganguli, S Nelaturi, I Matei, K Sricharan arXiv preprint arXiv:1707.04642, 2017 | 135 | 2017 |
| Large scale automated reading of frontal and lateral chest x-rays using dual convolutional neural networks J Rubin, D Sanghavi, C Zhao, K Lee, A Qadir, M Xu-Wilson arXiv preprint arXiv:1804.07839, 2018 | 110 | 2018 |
| Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings J Rubin, S Parvaneh, A Rahman, B Conroy, S Babaeizadeh Journal of electrocardiology 51 (6), S18-S21, 2018 | 92 | 2018 |
| Ischemic stroke lesion segmentation in CT perfusion scans using pyramid pooling and focal loss SM Abulnaga, J Rubin Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries …, 2019 | 76 | 2019 |
| Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings J Rubin, S Parvaneh, A Rahman, B Conroy, S Babaeizadeh 2017 Computing in cardiology (cinc), 1-4, 2017 | 64 | 2017 |
| An ensemble boosting model for predicting transfer to the pediatric intensive care unit J Rubin, C Potes, M Xu-Wilson, J Dong, A Rahman, H Nguyen, ... International journal of medical informatics 112, 15-20, 2018 | 63 | 2018 |
| Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation S Parvaneh, J Rubin, A Rahman, B Conroy, S Babaeizadeh Physiological measurement 39 (8), 084003, 2018 | 56 | 2018 |
| Towards a mobile and wearable system for predicting panic attacks J Rubin, H Eldardiry, R Abreu, S Ahern, H Du, A Pattekar, DG Bobrow Proceedings of the 2015 ACM International Joint Conference on Pervasive and …, 2015 | 37 | 2015 |
| Similarity-based retrieval and solution re-use policies in the game of Texas Hold’em J Rubin, I Watson International Conference on Case-Based Reasoning, 465-479, 2010 | 36 | 2010 |
| A wearable and mobile intervention delivery system for individuals with panic disorder L Cruz, J Rubin, R Abreu, S Ahern, H Eldardiry, DG Bobrow Proceedings of the 14th International Conference on Mobile and Ubiquitous …, 2015 | 35 | 2015 |
| Electrocardiogram monitoring and interpretation: from traditional machine learning to deep learning, and their combination S Parvaneh, J Rubin 2018 Computing in Cardiology Conference (CinC) 45, 1-4, 2018 | 31 | 2018 |
| Method and a system for providing hosted services based on a generalized model of a health/wellness program A Ram, GM Youngblood, PL Pirolli, LD Nelson, J Vig, SP Ahern, J Rubin, ... US Patent 9,898,789, 2018 | 31 | 2018 |
| CT-To-MR conditional generative adversarial networks for ischemic stroke lesion segmentation J Rubin, SM Abulnaga 2019 IEEE International conference on healthcare informatics (ICHI), 1-7, 2019 | 29 | 2019 |
| Detecting Steiner and linear isometries operads J Rubin Glasgow Mathematical Journal 63 (2), 307-342, 2021 | 27 | 2021 |
| System and method for automatic objective reporting via wearable sensors J Rubin, GM Youngblood, A Ram, PL Pirolli, J Vig, SP Ahern, LD Nelson US Patent 9,672,482, 2017 | 24 | 2017 |
| Case-based strategies in computer poker J Rubin, I Watson AI communications 25 (1), 19-48, 2012 | 24 | 2012 |
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