使用机器学习预测脊髓损伤预后:系统审查和元分析
Linxing Zhong1, Qiying Huang1, Hao Zhang1
1Department of Neurosurgery, Fuzong Clinical Medical College of Fujian Medical University, 156 West Second Ring North Road, Fuzhou, 350025, China, 86 13960760177, 86 591-87640785.
JMIR AI
|December 5, 2025
概括
机器学习 (ML) 模型在预测脊髓损伤 (SCI) 结果方面表现有希望,特别是在功能预后方面. 该XGBoost算法显示了最高的准确性,表明MLML.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 康复医学 康复医学 康复医学
背景情况:
- 脊髓损伤 (SCI) 在患者预后方面提出了复杂的挑战.
- 机器学习 (ML) 技术正在成为预测SCI结果的强大工具.
研究的目的:
- 评估ML模型在预测SCI后果方面的有效性和口径.
- 为了比较各种ML算法对SCI预后的性能.
主要方法:
- 在多个数据库 (PubMed,科学网,Embase等) 中进行全面的文献搜索. ) 的情况.
- 对ML模型的接收器操作特征曲线 (AUC) 下面区域的元分析.
- 包括13项符合条件的研究,来自1254篇获取的文章.
主要成果:
- XGBoost算法实现了脊髓功能预后的最高AUC (0.867).
- 其他ML模型在预测并发症,独立生活和行走能力方面表现不同.
- 随机森林和物流回归也证明了不同结果的显著预测能力.
结论:
- ML模型准确地预测SCI的结果,特别是脊髓功能预后.
- XGBoost算法是SCI预后的最高性能模型.
- 机器学习和大型数据集的进步将进一步提高临床医生的预测能力.
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