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

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/xgboost-a-machine-learning-method-predicts-neurological-recovery-in-patients/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Tomoo Inoue,Tomoo Inoue,Daisuke Ichikawa,Taro Ueno,Maxwell Cheong,Takashi Inoue,Takashi Inoue,William D. Whetstone,Toshiki Endo,Kuniyasu Nizuma,Teiji Tominaga |
| Citations | 98 |
| DOI | 10.1089/neur.2020.0009 |
| Fields | Engineering,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.liebertpub.com/doi/pdf/10.1089/neur.2020.0009 |
| OpenAlex ID | https://openalex.org/W3044845446 |
| PMID | 34223526 |
| Type | article |
| Year | 2020 |

## Paper authors

- [Taro Ueno](https://scholariq.org/researchers/taro-ueno/)

## Paper journal

- [Neurotrauma Reports](https://scholariq.org/journals/neurotrauma-reports/)

## Paper primary topic

- [Spinal Cord Injury Research](https://scholariq.org/topics/spinal-cord-injury-research/)

## Paper topics

- [Spinal Cord Injury Research](https://scholariq.org/topics/spinal-cord-injury-research/)
- [Medical Imaging and Analysis](https://scholariq.org/topics/medical-imaging-and-analysis/)
- [Spinal Fractures and Fixation Techniques](https://scholariq.org/topics/spinal-fractures-and-fixation-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.