The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System.

MLA

Nakatsugawa, Minoru, et al. “The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System.” International Journal of Radiation Oncology, Biology, Physics, vol. 103, no. 2, Feb. 2019, pp. 460–67. EBSCOhost, https://doi.org/10.1016/j.ijrobp.2018.09.038.

APA

Nakatsugawa, M., Cheng, Z., Kiess, A., Choflet, A., Bowers, M., Utsunomiya, K., Sugiyama, S., Wong, J., Quon, H., & McNutt, T. (2019). The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System. International Journal of Radiation Oncology, Biology, Physics, 103(2), 460–467. https://doi.org/10.1016/j.ijrobp.2018.09.038

Chicago

Nakatsugawa, Minoru, Zhi Cheng, Ana Kiess, Amanda Choflet, Michael Bowers, Kazuki Utsunomiya, Shinya Sugiyama, John Wong, Harry Quon, and Todd McNutt. 2019. “The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System.” International Journal of Radiation Oncology, Biology, Physics 103 (2): 460–67. doi:10.1016/j.ijrobp.2018.09.038.