Journal
npj Digital Medicine
Publication Date
6-14-2022
Volume
5
Issue
1
First Page
76
Document Type
Open Access Publication
DOI
10.1038/s41746-022-00615-8
Rights and Permissions
Tong, J., Luo, C., Islam, M.N. et al. Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites. npj Digit. Med. 5, 76 (2022). https://doi.org/10.1038/s41746-022-00615-8 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Recommended Citation
Tong, Jiayi; Luo, Chongliang; Islam, Md Nazmul; Sheils, Natalie E; Buresh, John; Edmondson, Mackenzie; Merkel, Peter A; Lautenbach, Ebbing; Duan, Rui; and Chen, Yong, "Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites." npj Digital Medicine. 5, 1. 76 (2022).
https://digitalcommons.wustl.edu/oa_4/58
Supplementary Material
41746_2022_615_MOESM2_ESM.pdf (2534 kB)
Reporting Summary
