| Federated learning of electronic health records to improve mortality prediction in hospitalized patients with COVID-19: machine learning approach A Vaid, SK Jaladanki, J Xu, S Teng, A Kumar, S Lee, S Somani, ... JMIR medical informatics 9 (1), e24207, 2021 | 143* | 2021 |
| Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients T Wanyan, H Honarvar, SK Jaladanki, C Zang, N Naik, S Somani, ... Patterns (2021): 100389., 2021 | 22 | 2021 |
| Deep learning with heterogeneous graph embeddings for mortality prediction from electronic health records T Wanyan, H Honarvar, A Azad, Y Ding, BS Glicksberg Data Intelligence 3 (3), 329-339, 2021 | 14 | 2021 |
| Relational learning improves prediction of mortality in COVID-19 in the intensive care unit T Wanyan, A Vaid, JK De Freitas, S Somani, R Miotto, GN Nadkarni, ... IEEE transactions on big data 7 (1), 38-44, 2020 | 10 | 2020 |
| Heterogenous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions T Wanyan, M Kang, MA Badgeley, KW Johnson, JKD Freitas, ... International Conference on Artificial Intelligence in Medicine. 2020, 2020 | 5 | 2020 |
| Bootstrapping your own positive sample: contrastive learning with electronic health record data T Wanyan, J Zhang, Y Ding, A Azad, Z Wang, BS Glicksberg arXiv preprint arXiv:2104.02932, 2021 | 4 | 2021 |
| Biomedical knowledge graph refinement and completion using graph representation learning and top-K similarity measure IA Ebeid, M Hassan, T Wanyan, J Roper, A Seal, Y Ding Diversity, Divergence, Dialogue: 16th International Conference, iConference …, 2021 | 4 | 2021 |
| Addressing supply chain risks of microelectronic devices through computer vision Z Chen, T Wanyan, R Rao, B Cutilli, J Sowinski, D Crandall, ... | 4 | 2017 |
| Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction T Wanyan, M Lin, E Klang, KM Menon, FF Gulamali, A Azad, Y Zhang, ... Proceedings of the 13th ACM International Conference on Bioinformatics …, 2022 | 3 | 2022 |
| Attribute2vec: deep network embedding through multi-filtering GCN T Wanyan, C Zhang, A Azad, X Liang, D Li, Y Ding arXiv preprint arXiv:2004.01375, 2020 | 3 | 2020 |
| Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation H Honarvar, C Agarwal, S Somani, A Vaid, J Lampert, T Wanyan, ... Cardiovascular digital health journal 3 (5), 220-231, 2022 | 2 | 2022 |
| Tractography Using Reinforcement Learning And Adaptive-Expanding Graphs T Wanyan, L Liu, E Garyfallidis International symposium on biomedical imaging, 2018 | 2 | 2018 |
| Relational Modeling of Electronic Health Record Data for Clinical Prediction T Wanyan Indiana University, 2022 | 1 | 2022 |
| Coupling Heterogeneous Graph Embeddings with Convolution Neural Networks Improves Mortality Prediction T Wanyan, Y Ding, A Azad, BS Glicksberg In Proceedings of ACM Conference (Conference’17). Association for Computing …, 2018 | 1 | 2018 |
| Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served populations M Lin, Y Xiao, B Hou, T Wanyan, MM Sharma, Z Wang, F Wang, ... AMIA Summits on Translational Science Proceedings 2023, 370, 2023 | | 2023 |
| Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning J Lee, T Wanyan, Q Chen, TDL Keenan, BS Glicksberg, EY Chew, Z Lu, ... International Workshop on Machine Learning in Medical Imaging, 11-20, 2022 | | 2022 |
| Predicting 2-year and 5-year Late AMD Progression using Deep Learning with Longitudinal Fundus Images J Lee, T Wanyan, Q Chen, TDL Keenan, EY Chew, Z Lu, F Wang, Y Peng Investigative Ophthalmology & Visual Science 63 (7), 3003–F0273-3003–F0273, 2022 | | 2022 |
| Important new insights for the reduction of false positives in tractograms emerge from streamline-based registration and pruning. T Wanyan, E Garyfallidis International Society for Magnetic Resonance in Medicine, 2017 | | 2017 |
Benjamin GlicksbergVP Data Science & Machine Learning, Character Biosciences | Adjunct Professor, Icahn School of在 mssm.edu 的电子邮件经过验证
