传统和机器学习模型用于预测缺血性中风中的出血转变:系统性审查和元分析
Yanan Wang1, Zengyi Zhang2, Zhimeng Zhang2
1Department of Neurology, West China Hospital, Sichuan University, No. 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China.
Systematic reviews
|February 22, 2025
概括
在缺血性中风后,鉴定出血转化 (HT) 风险较高的患者是具有挑战性的. 本综述比较了HT预测的传统和机器学习模型,发现了当前模型的局限性.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 血液转化 (HT) 是缺血性中风后的一种严重并发症.
- 准确预测HT风险至关重要,但仍然具有挑战性.
- 对于HT的现有预测模型缺乏全面的摘要.
研究的目的:
- 系统地审查和比较传统和基于机器学习的HT预测模型.
- 评估这些模型的开发,验证和诊断准确性.
- 确定局限性,并建议HT预测的未来方向.
主要方法:
- 在PubMed和Ovid-Embase系统搜索相关研究.
- 使用CHARMS检查清单进行数据提取,并使用PROBAST进行偏差风险评估.
- 对具有至少两项研究的外部验证和偏差风险较低的模型进行元分析.
主要成果:
- 包括100项研究:67项用于开发和33项用于验证.
- 分析了传统模型 (47) 和机器学习模型 (23).
- 对15项研究的元分析显示,HT预测的AUC约为0.70,许多研究具有高偏差风险.
结论:
- 传统的和机器学习的HT预测模型都在严谨性,准确性和适用性方面存在局限性.
- 未来的模型需要更严格的验证,标准化的报告和临床上有意义的预测因素.
- 需要共同努力在不同人群中进行验证,以提高临床效用.
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