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机器学习用于预测与败血症相关的死亡:系统审查和元分析
Yan Zhang1, Weiwei Xu2, Ping Yang3
1Department of Critical Care Medicine, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
BMC medical informatics and decision making
|December 12, 2023
概括
机器学习模型在预测败血症患者死亡率方面显示出有希望的准确性,其性能优于传统工具. 建议进一步开发这些模型,以改善临床风险评估.
科学领域:
- 临床信息学 临床信息学
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 败血症具有很高的短期死亡风险,但目前的评估工具的预测能力有限.
- 机器学习 (ML) 提供了预测败血症早期死亡风险的潜力,但缺乏对变量构造和方法性能的理解.
研究的目的:
- 系统地审查和元分析ML对败血症相关死亡的预测价值.
- 探索ML模型构建,并比较各种ML方法的性能.
主要方法:
- 我们对PubMed,Embase,Cochrane和Web of Science数据库进行了系统的搜索.
- 预测模型偏差风险评估工具 (PROBAST) 评估了模型偏差.
- 根据死亡时间和模型类型进行了子组分析.
主要成果:
- 分析了来自50项研究的104个ML模型,显示C指数组合为0.799 (培训) 和0.774 (验证).
- 机器学习模型表现出比传统的评分工具更高的性能.
- 随机森林 (RF) 和极度梯度提升 (XGBoost) 被确定为首选模型,乳酸盐是经常使用的预测因素.
结论:
- ML方法在预测败血症死亡风险方面表现出良好的准确性.
- 需要开发更新的死亡风险评估工具,利用特定临床环境的ML方法来评估死亡风险.
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