Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study

MLA

Huang, Chenxi, et al. “Enhancing the Prediction of Acute Kidney Injury Risk after Percutaneous Coronary Intervention Using Machine Learning Techniques: A Retrospective Cohort Study.” PLoS Medicine, vol. 15, no. 11, Nov. 2018, p. e1002703. EBSCOhost, https://doi.org/10.1371/journal.pmed.1002703.

APA

Huang, C., Murugiah, K., Mahajan, S., Li, S.-X., Dhruva, S. S., Haimovich, J. S., Wang, Y., Schulz, W. L., Testani, J. M., Wilson, F. P., Mena, C. I., Masoudi, F. A., Rumsfeld, J. S., Spertus, J. A., Mortazavi, B. J., & Krumholz, H. M. (2018). Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLoS Medicine, 15(11), e1002703. https://doi.org/10.1371/journal.pmed.1002703

Chicago

Huang, Chenxi, Karthik Murugiah, Shiwani Mahajan, Shu-Xia Li, Sanket S. Dhruva, Julian S. Haimovich, Yongfei Wang, et al. 2018. “Enhancing the Prediction of Acute Kidney Injury Risk after Percutaneous Coronary Intervention Using Machine Learning Techniques: A Retrospective Cohort Study.” PLoS Medicine 15 (11): e1002703. doi:10.1371/journal.pmed.1002703.