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Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

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 issue for developing ML models for the coronavirus-disease 2019 (COVID-19) pandemic where data is highly imbalanced, parti...

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書誌詳細
出版年:ArXiv
主要な著者: Wanyan, Tingyi, Honarvar, Hossein, Jaladanki, Suraj K., Zang, Chengxi, Naik, Nidhi, Somani, Sulaiman, Freitas, Jessica K. De, Paranjpe, Ishan, Vaid, Akhil, Miotto, Riccardo, Nadkarni, Girish N., Zitnik, Marinka, ArifulAzad, Wang, Fei, Ding, Ying, Glicksberg, Benjamin S.
フォーマット: Artigo
言語:Inglês
出版事項: Cornell University 2021
主題:
オンライン・アクセス:https://ncbi.nlm.nih.gov/pmc/articles/PMC7805456/
https://ncbi.nlm.nih.gov/pubmed/33442560
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