Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation-Reference-Cited by-同舟云学术

Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation

Published:2023-08-01 Issue:15-16 Volume:40 Page:1694-1706
ISSN:0897-7151
Container-title:Journal of Neurotrauma
language:en
Short-container-title:Journal of Neurotrauma

Author:

Matsuo Kazuya¹²,Aihara Hideo³,Hara Yoshie¹,Morishita Akitsugu⁴,Sakagami Yoshio⁵,Miyake Shigeru⁶,Tatsumi Shotaro⁷,Ishihara Satoshi⁸,Tohma Yoshiki⁹,Yamashita Haruo¹,Sasayama Takashi²

Affiliation:

1. Department of Neurosurgery, Hyogo Emergency Medical Center and Kobe Red Cross Hospital, Kobe, Japan.

2. Department of Neurosurgery, Kobe University Graduate School of Medicine, Kobe, Japan.

3. Department of Neurosurgery, Hyogo Prefectural Himeji Cardiovascular Center, Himeji, Japan.

4. Department of Neurosurgery, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan.

5. Department of Neurosurgery, Hyogo Prefectural Awaji Medical Center, Sumoto, Japan.

6. Department of Neurosurgery, Kita-harima Medical Center, Ono, Japan.

7. Department of Neurosurgery, Hirohata Steel Memorial Hospital, Himeji, Japan.

8. Department of Emergency and Critical Care Medicine, Hyogo Emergency Medical Center, Kobe, Japan.

9. Acute Care Medical Center, Hyogo Prefectural Kakogawa Medical Center, Kakogawa, Japan.

Publisher

Mary Ann Liebert Inc

Subject

Neurology (clinical)

Link

https://www.liebertpub.com/doi/pdf/10.1089/neu.2022.0515

Reference50 articles.

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2. Early management of severe traumatic brain injury

3. 4. Basso A, Previgliano I, Servadei F. Traumatic brain injuries. In: Neurological Disorders: Public Health Challenges. (World Health Organization eds.) WHO Press: Geneva, Switzerland; 2006; pp. 164–173.

4. Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

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