在患有败血症相关急性损伤的患者中,基于机器学习的死亡风险预测模型:系统性审查
Xu Li1,2, Xu Hu1, Huiting Xu2,3
1Department of Central ICU, The First Affiliated Hospital of Soochow University, Suzhou, China.
Frontiers in medicine
|October 24, 2025
概括
机器学习模型对预测败血症相关急性损伤 (SA-AKI) 死亡率充满希望. 然而,方法上的不一致性和潜在的偏见需要对临床整合进行标准化.
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
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 机器学习 (ML) 模型越来越多地用于预测败血症相关急性损伤 (SA-AKI) 的死亡率.
- 虽然有效,但ML模型显示质量的不一致性,需要进行彻底评估.
- 本次审查重点关注SA-AKI死亡率的ML模型的开发质量,方法严谨性和预测性能.
研究的目的:
- 系统地评估基于ML的SA-AKI患者的死亡风险预测模型.
- 评估SA-AKI死亡率预测中现有的ML模型的质量,方法和预测性能.
主要方法:
- 在主要数据库 (PubMed,Cochrane,Embase,Web of Science) 进行了全面的文献搜索,直到2025年7月.
- 两个独立的研究人员选了研究,提取了数据,并使用人工智能偏差评估工具的预测模型风险评估工具评估了模型质量.
- 包括9项涉及ML模型开发和SA-AKI死亡率验证的研究.
主要成果:
- 九项研究使用了18种算法,其中随机森林和极端梯度增强是最常见的.
- 内部验证的AUC在0.70-0.87之间,而外部验证显示了极端梯度增强的强大性能.
- 关键预测因素包括人口统计,生命体征,实验室测试,疾病严重程度,并发症和干预措施.
结论:
- 机器学习模型显示了SA-AKI死亡率预测与一致的核心预测因素的前景.
- 大多数研究都表现出潜在的偏见,突出了对标准化数据处理,特征选择和验证的需求.
- 未来的研究应该专注于前模型,早期变量,可解释性和临床整合.
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