Article Versions Notes

Burdick H, Lam C, Mataraso S, Siefkas A, Braden G, Dellinger RP, McCoy A, Vincent J-L, Green-Saxena A, Barnes G, et al. Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. Journal of Clinical Medicine. 2020; 9(12):3834. https://doi.org/10.3390/jcm9123834

Burdick, Hoyt, Carson Lam, Samson Mataraso, Anna Siefkas, Gregory Braden, R. Phillip Dellinger, Andrea McCoy, Jean-Louis Vincent, Abigail Green-Saxena, Gina Barnes, and et al. 2020. "Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial" Journal of Clinical Medicine 9, no. 12: 3834. https://doi.org/10.3390/jcm9123834

Burdick, H., Lam, C., Mataraso, S., Siefkas, A., Braden, G., Dellinger, R. P., McCoy, A., Vincent, J.-L., Green-Saxena, A., Barnes, G., Hoffman, J., Calvert, J., Pellegrini, E., & Das, R. (2020). Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. Journal of Clinical Medicine, 9(12), 3834. https://doi.org/10.3390/jcm9123834