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Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.

T. Wanyan, H. Honarvar, A. Azad, Y. Ding, and B. Glicksberg. Data Intell., 3 (3): 329-339 (2021)

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Contrastive learning improves critical event prediction in COVID-19 patients.T. Wanyan, H. Honarvar, S. Jaladanki, C. Zang, N. Naik, S. Somani, J. Freitas, I. Paranjpe, A. Vaid, J. Zhang and 8 other author(s). Patterns, 2 (12): 100389 (2021)Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.T. Wanyan, A. Vaid, J. Freitas, S. Somani, R. Miotto, G. Nadkarni, A. Azad, Y. Ding, and B. Glicksberg. IEEE Trans. Big Data, 7 (1): 38-44 (2021)Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction.T. Wanyan, M. Lin, E. Klang, K. Menon, F. Gulamali, A. Azad, Y. Zhang, Y. Ding, Z. Wang, F. Wang and 2 other author(s). BCB, page 9:1-9:9. ACM, (2022)Biomedical Knowledge Graph Refinement and Completion Using Graph Representation Learning and Top-K Similarity Measure.I. Ebeid, M. Hassan, T. Wanyan, J. Roper, A. Seal, and Y. Ding. iConference (1), volume 12645 of Lecture Notes in Computer Science, page 112-123. Springer, (2021)Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN.T. Wanyan, C. Zhang, A. Azad, X. Liang, D. Li, and Y. Ding. CoRR, (2020)Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction.T. Wanyan, M. Lin, Y. Ding, B. Glicksberg, F. Wang, and Y. Peng. ICHI, page 101-110. IEEE, (2023)Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations.M. Lin, Y. Xiao, B. Hou, T. Wanyan, M. Sharma, Z. Wang, F. Wang, S. Tassel, and Y. Peng. CoRR, (2023)Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.T. Wanyan, H. Honarvar, A. Azad, Y. Ding, and B. Glicksberg. Data Intell., 3 (3): 329-339 (2021)Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data.T. Wanyan, J. Zhang, Y. Ding, A. Azad, Z. Wang, and B. Glicksberg. CoRR, (2021)Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning.J. Lee, T. Wanyan, Q. Chen, T. Keenan, B. Glicksberg, E. Chew, Z. Lu, F. Wang, and Y. Peng. MLMI@MICCAI, volume 13583 of Lecture Notes in Computer Science, page 11-20. Springer, (2022)

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Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.

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Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.