XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury-Reference-Cited by-同舟云学术

XGBoost, a Machine Learning Method, Predicts Neurological Recovery in Patients with Cervical Spinal Cord Injury

Published:2020-01-01 Issue:1 Volume:1 Page:8-16
ISSN:2689-288X
Container-title:Neurotrauma Reports
language:en
Short-container-title:Neurotrauma Reports

Author:

Inoue Tomoo¹,Ichikawa Daisuke²,Ueno Taro²,Cheong Maxwell³,Inoue Takashi¹,Whetstone William D.⁴,Endo Toshiki¹⁵,Nizuma Kuniyasu⁵⁶⁷,Tominaga Teiji⁵

Affiliation:

1. Department of Neurosurgery, National Health Organization Sendai Medical Center, Sendai, Miyagi, Japan.

2. SUSMED, Inc., Tokyo, Japan.

3. Department of Radiology, Stanford University School of Medicine, Palo Alto, California, USA.

4. Department of Emergency Medicine, University of California, San Francisco, San Francisco, California, USA.

5. Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.

6. Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Miyagi, Japan.

7. Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.

Publisher

Mary Ann Liebert Inc

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.liebertpub.com/doi/pdf/10.1089/neur.2020.0009

Reference49 articles.

1. Incidence and Prevalence of Spinal Cord Injury in Canada: A National Perspective

2. Longitudinal Prediction of Quality-of-Life Scores and Locomotion in Individuals With Traumatic Spinal Cord Injury

3. The Elements of Statistical Learning

4. Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury

5. Analysis of Machine Learning Techniques for Heart Failure Readmissions

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