基于机器学习的COVID-19预后模型在报告质量方面落后:来自TRIPOD/TRIPOD + AI系统审查的发现
Ioannis Partheniadis1,2, Persefoni Talimtzi2, Adriani Nikolakopoulou3,4
1Laboratory of Pharmaceutical Technology, School of Pharmacy, Faculty of Health Sciences, Aristotle University of Thessaloniki, University Campus, Thessaloniki, 54124, Greece.
Diagnostic and prognostic research
|February 3, 2026
概括
报告COVID-19预后模型的情况很差,特别是在机器学习方面. 遵守TRIPOD和TRIPOD+AI等指南的程度较低,阻碍了清晰度和临床价值.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 人工智能在医学中的应用
背景情况:
- 对于COVID-19预后模型的确立报告标准经常没有得到满足.
- 个人诊断或预后多变量预测模型 (TRIPOD) 清单的透明报告及其2024年AI扩展 (TRIPOD+AI) 为质量评估提供了框架.
- 这项研究比较了 COVID-19 常规和机器学习 (ML) 预后模型之间的报告完整性.
研究的目的:
- 评估和比较使用传统统计方法与机器学习算法开发的COVID-19预后模型的报告完整性.
- 确定这些模型报告中的具体缺陷领域.
主要方法:
- 在MEDLINE,Epistemonikos.org和Scopus中进行了系统的文献搜索,截至2024年7月31日.
- 包括报告COVID-19预后模型开发和验证的研究.
- 传统模型使用TRIPOD进行评估;ML模型使用TRIPOD+AI进行评估,数据提取遵循检查列表项.
主要成果:
- 总共分析了53项研究 (71个模型),显示出对TRIPOD和TRIPOD+AI指导方针的遵守程度较低.
- 与传统模型 (38.1%) 相比,机器学习模型的报告完整性明显较差 (28.4%).
- 没有一项研究完全遵守抽象报告,样本大小计算普遍没有报告;在所有研究中,方法和结果报告都很差.
结论:
- 在ML研究中较低的坚持率可能归因于最近出版的TRIPOD+AI.AI.
- 传统的和基于ML的COVID-19预测模型都显示出报告不足,模型描述和性能存在关键差距.
- 加强对报告准则的遵守对于提高预后预测模型的清晰度,可重复性和临床实用性至关重要.
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