构建和验证用于预测急性胰腺炎患者的败血症的机器学习模型
Fei Liu1, Jie Yao2, Chunyan Liu3
1Department of Emergency Medicine, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, P.R. China.
BMC surgery
|September 1, 2023
概括
机器学习,特别是渐变增强决策树 (GBDT) 模型,有效预测急性胰腺炎 (AP) 患者的败血症风险,优于早期干预的传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 关键护理医学 关键护理医学
背景情况:
- 急性胰腺炎 (AP) 构成发生败血症的重大风险.
- 在AP患者中早期和准确预测败血症对于及时干预和改善结果至关重要.
研究的目的:
- 开发和比较机器学习 (ML) 模型,用于预测AP患者的败血症风险.
- 对传统的物流回归 (LR) 和已建立的评分系统来评估ML模型的性能.
主要方法:
- 一项回顾性队列研究利用了来自MIMIC III和MIMIC IV数据库的数据.
- 最少绝对收缩和选择操作员 (LASSO) 回归和5倍交叉验证确定了关键预测因素.
- 包括渐变增强决策树 (GBDT) 在内的六个ML模型被构建并使用AUC,灵敏度和特异性等指标进行比较.
主要成果:
- 在GBDT模型中,AP患者的败血症预测AUC达到0.985.
- 关键预测因素包括年龄,生命体征和实验室值 (例如,GCS,心率,RDW,BUN).
- 在GBDT模型中,比LR和评分系统 (SOFA,qSOFA,SAPS II,SIRS,BISAP) 显示出更高的性能.
结论:
- GBDT模型为AP患者的早期败血症风险预测提供了一个强大而准确的工具.
- 这种ML方法可以帮助临床医生识别高风险个体,以便及时进行治疗干预.
- 这些发现突显了ML在提高关键护理决策的潜力,以改善与AP相关的败血症.
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