基于机器学习的术后恶心和吐在接受镇静性胃肠内镜的患者的预测
Yongchao Yao1, Yanna Li1, Fei Xing1,2
1Department of Anesthesiology, Pain and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Annals of medicine
|December 8, 2025
概括
使用线性差异分析的机器学习模型有效预测内镜患者手术后恶心和吐 (PONV) 风险. 该工具通过识别关键风险因素,如先前的PONV病史和阿片类药物使用,来帮助个性化术后护理.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 临床决策支持系统 临床决策支持系统
背景情况:
- 手术后恶心和吐 (PONV) 对于接受镇静性胃肠内镜检查的患者来说仍然是一个重大问题.
- 准确预测PONV对于有效的术后管理和患者舒适性至关重要.
- 现有的预测方法可能缺乏通用性或实时适用性.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测经术后恶心和吐 (PONV) 在接受镇静性胃肠内镜的患者中.
- 为了比较用于PONV预测的多个ML算法的性能.
- 使用SHAP分析增强模型的解释性,并将其部署为用户友好的基于Web的工具.
主要方法:
- 一项多中心前性研究招募了中国四家三级医院的745名患者.
- 通过使用人口,临床和程序数据,训练和验证了11个ML算法.
- 模型性能使用AUC,准确性,精度,回忆,F1得分,特异性和科恩的kappa进行评估,并使用SHAP分析进行解释性.
主要成果:
- 线性差异分析 (LDA) 在外部验证中表现出优越的概括性 (AUC:0.834),优于其他模型.
- 通过SHAP分析确定的关键预测因素包括先前的PONV病史,身高,检查类型和阿片类药物使用.
- 优化的LDA模型被翻译成一个实时的,基于Web的风险预测工具.
结论:
- 线性差异分析 (LDA) 为预测内镜患者的PONV提供了卓越的概括性.
- 开发的基于网络的工具提供实时PONV风险评估.
- 这种基于机器学习的方法支持个性化的外科手术期间管理策略.
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