基于机器学习的风险预测模型构建了ICU中机械通风患者难以断奶的风险预测模型
Huimei Xu1, Yanyan Ma1, Yan Zhuang2
1Yangzhou University, School of Nursing, School of Public Health, Yangzhou, China.
Scientific reports
|September 6, 2024
概括
机器学习准确地预测了机械通风 (MV) 的重症监护病房 (ICU) 患者难以断奶的情况. 随机森林模型确定了有风险的患者,改善了呼吸道管理和患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
背景情况:
- 在重症监护室 (ICU) 患者的机械通风 (MV) 难以断奶导致并发症,更长的住院时间和成本增加.
- 早期识别患有难以断奶风险的患者对于及时干预和改善患者管理至关重要.
研究的目的:
- 开发一种基于机器学习的风险预测模型,用于MV的ICU患者难以断奶的风险预测.
- 为了确定难以断奶的重要影响因素,以加强呼吸道管理.
主要方法:
- 收集了来自MV的487名ICU患者的数据,记录了36个变量.
- 为了风险预测,比较了五种机器学习模型 (逻辑回归,随机森林,SVM,LGBM,XGBoost).
- 使用AUC,精度,回忆,特异性和F1分数的评估模型.
主要成果:
- 随机森林算法在预测难断奶时表现最好.
- 实现了0.805的曲线下面积 (AUC) 和0.748.8的精度.
- 确定了难以断奶的重要影响因素,使得能够创建一个强大的风险预测模型.
结论:
- 基于机器学习的风险预测模型,特别是使用随机森林,可以有效地识别患有难以断奶的高风险的ICU患者.
- 该模型有助于临床医生准确评估风险,并制定个性化治疗计划.
- 这些发现支持降低难断奶的风险,提高患者的生活质量.
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