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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)

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Benjamin S J Wolpert

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams.K. Shameer, M. Badgeley, R. Miotto, B. Glicksberg, J. Morgan, and J. Dudley. Briefings Bioinform., 18 (1): 105-124 (2017)PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.B. Glicksberg, B. Oskotsky, P. Thangaraj, N. Giangreco, M. Badgeley, K. Johnson, D. Datta, V. Rudrapatna, N. Rappoport, M. Shervey and 12 other author(s). Bioinform., 35 (21): 4515-4518 (2019)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, R. Miotto and 6 other author(s). CoRR, (2021)Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.J. Freitas, K. Johnson, E. Golden, G. Nadkarni, J. Dudley, E. Bottinger, B. Glicksberg, and R. Miotto. Patterns, 2 (9): 100337 (2021)A foundational vision transformer improves diagnostic performance for electrocardiograms.A. Vaid, J. Jiang, A. Sawant, S. Lerakis, E. Argulian, Y. Ahuja, J. Lampert, A. Charney, H. Greenspan, J. Narula and 2 other author(s). npj Digit. Medicine, (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. 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)Extracting Social Isolation Information From Psychiatric Notes in the Electronic Health Records.L. Lepow, B. Patra, I. Landi, P. Adekkanattu, J. Pathak, M. Olfson, J. Mann, E. Ryu, J. Biernacka, G. Nadkarni and 4 other author(s). AMIA, AMIA, (2021)Biological research and self-driving labs in deep space supported by artificial intelligence.L. Sanders, R. Scott, J. Yang, A. Qutub, H. Martín, D. Berrios, J. Hastings, J. Rask, G. Mackintosh, A. Hoarfrost and 46 other author(s). Nat. Mac. Intell., 5 (3): 208-219 (March 2023)Federated Learning for Healthcare Informatics.J. Xu, B. Glicksberg, C. Su, P. Walker, J. Bian, and F. Wang. J. Heal. Informatics Res., 5 (1): 1-19 (2021)

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Disambiguation of "Glicksberg, Benjamin S."

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Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction.

Please choose a person to relate this publication to

Benjamin S J Wolpert

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Glicksberg, Benjamin S."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction.

Please choose a person to relate this publication to

Benjamin S J Wolpert

S Danischewski

S Hüttelmaier

S Leopold

S Steinlechner

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Optimizing Embedding Space with Sub-categorical Supervised Pre-training: A Theoretical Approach Towards Improving Sepsis Prediction.