通过机器学习方法,通过机器学习方法识别 mitra valve 手术后感染的风险因素
Ningjie Zhang1, Kexin Fan2, Hongwen Ji3
1Department of Blood Transfusion, The Second Xiangya Hospital, Central South University, Changsha, China.
Frontiers in cardiovascular medicine
|June 29, 2023
概括
机器学习确定了预测米特拉手术后术后感染的主要因素. 这些发现有助于医生实施预防措施,降低感染风险.
科学领域:
- 心血管外科心血管外科
- 医疗信息学 医疗信息学
- 预防传染病 预防传染病
背景情况:
- 手术后感染是心脏手术后的一个严重并发症.
- 确定关键变量对于有效的干预策略至关重要.
- 密特拉手术对感染控制提出了特定的挑战.
研究的目的:
- 利用机器学习来识别与手术后感染相关的关键外科变量.
- 使用已识别的变量开发术后感染的预测模型.
- 增强心脏手术患者预防感染的临床决策.
主要方法:
- 从1223名接受心脏膜手术的患者中分析了91个人口统计和周围手术参数.
- 应用随机森林 (RF) 和最小绝对收缩和选择操作员 (LASSO) 进行变量选择.
- 开发和评估多种机器学习模型,包括RF,XGBoost,SVM等,使用ROC曲线和AUC.
主要成果:
- 确定21个重叠的关键变量,包括年龄,输血 (RBC,FFP),NYHA类,肌,LVEF和手术时间 (ACC).
- 机器学习模型显示出优异的歧视性能,AUC> 0.79.
- 选择的特征准确地预测了测试组中的感染风险.
结论:
- 机器学习有效地识别了手术后感染的关键预测因子.
- 开发的模型为风险分层和个性化预防策略提供了有价值的工具.
- 准确预测感染有助于及时进行干预,可能降低患者的发病率和医疗费用.
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