Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction-Reference-Cited by-同舟云学术

Supervised pretraining through contrastive categorical positive samplings to improve COVID-19 mortality prediction

Published:2022-08-07 Issue: Volume: Page:
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Container-title:Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
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Author:

Wanyan Tingyi¹,Lin Mingquan¹,Klang Eyal²,Menon Kartikeya M.²,Gulamali Faris F.²,Azad Ariful³,Zhang Yiye¹,Ding Ying⁴,Wang Zhangyang⁴,Wang Fei¹,Glicksberg Benjamin²,Peng Yifan¹

Affiliation:

1. Weill Cornell Medicine

2. Icahn School of Medicine at Mount Sinai

3. Indiana University

4. University of Texus Austin

Funder

National Library of Medicine

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3535508.3545541

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2. Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes

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4. Utilization of Deep Learning for Subphenotype Identification in Sepsis-Associated Acute Kidney Injury

5. Mayee F Chen , Daniel Y Fu , Avanika Narayan , Michael Zhang , Zhao Song , Kayvon Fatahalian , and Christopher Ré . 2022 . Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. arXiv preprint arXiv:2204.07596 (2022). Mayee F Chen, Daniel Y Fu, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, and Christopher Ré. 2022. Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning. arXiv preprint arXiv:2204.07596 (2022).

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