Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative.

MLA

Zang, Chengxi, et al. “Data-Driven Analysis to Understand Long COVID Using Electronic Health Records from the RECOVER Initiative.” Nature Communications, vol. 14, no. 1, Apr. 2023, p. 1948. EBSCOhost, https://doi.org/10.1038/s41467-023-37653-z.

APA

Zang, C., Zhang, Y., Xu, J., Bian, J., Morozyuk, D., Schenck, E. J., Khullar, D., Nordvig, A. S., Shenkman, E. A., Rothman, R. L., Block, J. P., Lyman, K., Weiner, M. G., Carton, T. W., Wang, F., & Kaushal, R. (2023). Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiative. Nature Communications, 14(1), 1948. https://doi.org/10.1038/s41467-023-37653-z

Chicago

Zang, Chengxi, Yongkang Zhang, Jie Xu, Jiang Bian, Dmitry Morozyuk, Edward J Schenck, Dhruv Khullar, et al. 2023. “Data-Driven Analysis to Understand Long COVID Using Electronic Health Records from the RECOVER Initiative.” Nature Communications 14 (1): 1948. doi:10.1038/s41467-023-37653-z.