USE OF MACHINE LEARNING METHODOLOGY TO FIND PREDICTORS OF ALL-CAUSE MORTALITY IN THE SYSTOLIC BLOOD PRESSURE INTERVENTION TRIAL (SPRINT).

MLA

Gani, Nuha, et al. “Use of Machine Learning Methodology to Find Predictors of All-Cause Mortality in the Systolic Blood Pressure Intervention Trial (Sprint).” Journal of the American College of Cardiology (JACC), vol. 75, Mar. 2020, p. 2061. EBSCOhost, https://doi.org/10.1016/S0735-1097(20)32688-7.

APA

Gani, N., Dandi, G., Mallick, Z., Ashraf, A., Xin, V., Atkinson, I., Husain, A., Hasan, N., Tian, X., Wu, C., Patel, T., Kettermann, A., Sopko, G., Cure, C., Csako, G., Jateng, D., Fleg, J., Crentsil, V., Chowdhury, I., & Burkhart, K. (2020). Use of Machine Learning Methodology to Find Predictors of All-Cause Mortality in the Systolic Blood Pressure Intervention Trial (Sprint). Journal of the American College of Cardiology (JACC), 75, 2061. https://doi.org/10.1016/S0735-1097(20)32688-7

Chicago

Gani, Nuha, Gauri Dandi, Zyannah Mallick, Adrita Ashraf, Victoria Xin, Ian Atkinson, Anwar Husain, et al. 2020. “Use of Machine Learning Methodology to Find Predictors of All-Cause Mortality in the Systolic Blood Pressure Intervention Trial (Sprint).” Journal of the American College of Cardiology (JACC) 75 (March): 2061. doi:10.1016/S0735-1097(20)32688-7.