基于机器学习的败血症相关急性损伤的预测模型:一个范围审查
Jie Li1,2, Manli Zhu1,2, Li Yan1,2
1Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Renal failure
|July 31, 2024
概括
机器学习显示出预测毒症相关急性损伤的前景. 然而,目前的研究质量和模型实用性需要改进,以获得更好的临床应用.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 在重症监护中的预测建模.
背景情况:
- 机器学习 (ML) 在识别,分级,诊断和确定败血症相关急性损伤 (AKI) 的预后方面提供了潜在的突破.
- 人工智能开发正在推动对急症护理条件的预测建模的进步,如败血症-AKI.
研究的目的:
- 系统地审查和批判性地评估基于ML的预测模型,用于与败血症相关的AKI.
- 识别用于败血症-AKI研究的常见ML算法,预测因素和目标.
- 评估现有预测模型的质量并确定其局限性.
主要方法:
- 在PubMed,Cochrane Library,Embase,Web of Science和Scopus数据库中进行系统的文献搜索.
- 关于ML算法,预测器和预测结果的全面数据提取.
- 批判性评估研究质量,数据聚合和分析方法.
主要成果:
- 从2898个初始记录中,确定了25个ML预测模型的ML预测模型.
- 传统的后勤回归和极端梯度提升是最常见的ML算法.
- 模型主要侧重于早期识别 (60%),预后 (32%) 和亚型识别 (8%),使用血清肌素,乳酸和年龄等预测因素.
结论:
- 现有的关于非死亡预后,长期结果和亚型鉴定模型的研究不足.
- 低质量的研究,单一的数据来源和不充分的偏见/实用性评估阻碍了模型的实用性.
- 研究质量和模型临床实用性的紧急改进对于败血症-AKI预测是必要的.
相关概念视频
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