基于机器学习的急性胰腺炎预后模型的系统审查:改善方法和报告质量的方向
Brian Critelli1, Amier Hassan1, Ila Lahooti2
1Department of Gastroenterology and Hepatology, Weill Cornell Medical College, New York, New York, United States of America.
PLoS medicine
|February 24, 2025
概括
机器学习 (ML) 模型对预测急性胰腺炎 (AP) 结果充满希望,但最近的研究显示,方法和报告质量存在重大差距. 改善这些方面对于临床信任和实施至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床预后 临床预后
背景情况:
- 准确的预后工具对于临床决策和急性胰腺炎 (AP) 个性化医疗至关重要.
- 现有的AP预测工具不足,促使人们探索机器学习 (ML) 技术.
- 高质量的方法和报告对于基于ML的预后模型的有效性和实施至关重要.
研究的目的:
- 系统地审查和批判性地评估最近发表的AP的基于ML的预后模型的方法和报告质量.
- 确定这些模型的开发和报告中的常见缺陷.
主要方法:
- 对2021年1月至2023年12月期间发表的AP预后模型的ML研究进行了系统审查.
- 这些研究来自MEDLINE,EMBASE,medRxiv,bioRxiv和arXiv.
- 使用预测模型偏差风险评估工具评估方法质量,并根据TRIPOD+AI声明评估报告质量.
主要成果:
- 包括30项研究,开发了39个新的ML模型来预测AP,主要预测严重程度或死亡率.
- 所有模型都表现出偏差的高风险,特别是在分析领域,许多模型都未能将过度乐观的表现降到最低.
- 报告质量不佳,仅涉及27个TRIPOD+AI项目中的15个,在样本大小证明,数据质量评估和代码共享方面存在重大遗漏.
结论:
- 方法和报告瘟疫的重大缺陷最近发表了基于ML的AP的预后模型.
- 这些缺陷破坏了这些有前途的模型的有效性,可重复性和临床实施.
- 解决这些质量问题对于实现ML在AP预测中的全部潜力至关重要.
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