226 citations · 236 across the 9 of their papers we have counts for
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cs.LG2021
SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
Chengxi Zang, Fei Wang
Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised con…
cs.LG2021
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…