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随机森林算法用于预测老年患者的手术后妄想症.

Weixuan Sheng1, Xianshi Tang2, Xiaoyun Hu1

  • 1Department of Anesthesiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.

Frontiers in neurology
|January 26, 2024
PubMed
概括

机器学习准确地预测了老年人手术后妄想 (POD). 确定的关键风险因素包括血清肌素 (CREA) 和术后疼痛评分 (VAS-Move-Max),可以更好地评估风险.

关键词:
混矩阵是一个混矩阵.年龄较大的患者患者.部分依赖图的部分依赖图.术后妄症 术后妄症随机的森林随机的森林

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科学领域:

  • 老年医学 老年医学
  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学

背景情况:

  • 手术后痴呆症 (POD) 是老年患者常见的并发症,与不良结果相关.
  • 准确预测POD对于及时干预和改善患者管理至关重要.

研究的目的:

  • 用机器学习算法识别老年患者POD的显著预测因素.
  • 开发和验证POD发生的高性能预测模型.

主要方法:

  • 随机对照试验数据集的二次分析.
  • 使用Boruta函数进行可变选和四种机器学习模型:物流回归 (LR),K-最近邻居 (KNN),分类和回归树 (CART) 和随机森林 (RF).
  • 采用了包括重复交叉验证,超参数优化和合成少数超采样技术 (Smote) 在内的技术,使用混矩阵,ROC和PRC曲线评估性能.

主要成果:

  • 确定了关键的预测变量,包括手术前的MMSE,查尔森得分,血清肌素 (CREA) 和术后的疼痛得分 (VAS-Move-Max).
  • 随机森林 (RF) 模型表现出卓越的性能,精度为0.9878和AUC-ROC/AUC-PRC为1.0.
  • CREA和VAS-Move-Max被确定为POD发展的主要风险因素.

结论:

  • 一种高性能机器学习算法已成功开发和验证,用于预测外科手术期间老年患者的POD风险.
  • 该研究强调了机器学习在识别关键风险因素和提高POD预测准确度方面的实用性.
  • 这些发现为临床医生提供了有价值的工具,以评估和管理老年人群中POD风险.