497 Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning

MLA

Shane Shahrestani, et al. “497 Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.” Neurosurgery, vol. 68, Mar. 2022, pp. 124–25. EBSCOhost, https://doi.org/10.1227/neu.0000000000001880_497.

APA

Shane Shahrestani, Nathan A. Shlobin, Nolan J. Brown, Alexander Himstead, Seth C. Ransom, Emily Ton, Darrin J. Lee, Peter Chiarelli, Carli Bullis, & Jason Chu. (2022). 497 Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning. Neurosurgery, 68, 124–125. https://doi.org/10.1227/neu.0000000000001880_497

Chicago

Shane Shahrestani, Nathan A. Shlobin, Nolan J. Brown, Alexander Himstead, Seth C. Ransom, Emily Ton, Darrin J. Lee, Peter Chiarelli, Carli Bullis, and Jason Chu. 2022. “497 Developing Predictive Models to Anticipate Shunt Complications in 33,248 Pediatric Patients with Shunted Hydrocephalus Utilizing Machine Learning.” Neurosurgery 68 (March): 124–25. doi:10.1227/neu.0000000000001880_497.