Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 p...

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Bibliografiset tiedot
Julkaisussa:medRxiv
Päätekijät: Vaid, Akhil, Jaladanki, Suraj K, Xu, Jie, Teng, Shelly, Kumar, Arvind, Lee, Samuel, Somani, Sulaiman, Paranjpe, Ishan, De Freitas, Jessica K, Wanyan, Tingyi, Johnson, Kipp W, Bicak, Mesude, Klang, Eyal, Kwon, Young Joon, Costa, Anthony, Zhao, Shan, Miotto, Riccardo, Charney, Alexander W, Böttinger, Erwin, Fayad, Zahi A, Nadkarni, Girish N, Wang, Fei, Glicksberg, Benjamin S
Aineistotyyppi: Artigo
Kieli:Inglês
Julkaistu: Cold Spring Harbor Laboratory 2020
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Article
Linkit:https://ncbi.nlm.nih.gov/pmc/articles/PMC7430624/
https://ncbi.nlm.nih.gov/pubmed/32817979
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1101/2020.08.11.20172809
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