Machine learning algorithms for improved prediction of in-hospital outcomes after moderate-to-severe traumatic brain injury: a Chinese retrospective cohort study
Author:
Publisher
Springer Science and Business Media LLC
Subject
Neurology (clinical),Surgery
Link
https://link.springer.com/content/pdf/10.1007/s00701-023-05647-x.pdf
Reference28 articles.
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3. Bruschetta R, Tartarisco G, Lucca LF et al (2022) Predicting outcome of traumatic brain injury: is machine learning the best way? Biomedicines 10(3)
4. Carney N, Totten AM, O'Reilly C et al (2017) Guidelines for the management of severe traumatic brain injury, fourth edition. Neurosurgery 80(1):6–15
5. Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785–794
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury;European Journal of Trauma and Emergency Surgery;2024-02-14
2. Machine learning models for predicting in-hospital outcomes after non-surgical treatment among patients with moderate-to-severe traumatic brain injury;Journal of Clinical Neuroscience;2024-02
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