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Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions.

T. Wanyan, M. Kang, M. Badgeley, K. Johnson, J. Freitas, F. Chaudhry, A. Vaid, S. Zhao, R. Miotto, G. Nadkarni, F. Wang, J. Rousseau, A. Azad, Y. Ding, and B. Glicksberg. AIME, volume 12299 of Lecture Notes in Computer Science, page 14-25. Springer, (2020)

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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)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)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)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)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. CoRR, (2020)Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records.T. Wanyan, H. Honarvar, A. Azad, Y. Ding, and B. Glicksberg. CoRR, (2020)Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions.T. Wanyan, M. Kang, M. Badgeley, K. Johnson, J. Freitas, F. Chaudhry, A. Vaid, S. Zhao, R. Miotto, G. Nadkarni and 5 other author(s). AIME, volume 12299 of Lecture Notes in Computer Science, page 14-25. Springer, (2020)

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Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions.

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Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions.