基于机器学习的30天心力衰竭患者再入院预测模型:系统性审查
1Department of Nursing, Graduate School of Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, 06974 Seoul, South Korea.
European journal of cardiovascular nursing
|February 29, 2024
概括
机器学习模型可以预测30天的心力衰竭再入院. 关键因素包括患者的人口统计学,病史和生命体征,帮助护士进行个性化护理规划.
科学领域:
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 心力衰竭 (HF) 的再入院是医疗保健的重大负担.
- 护士在管理HF患者护理和减少再入院方面发挥着至关重要的作用.
- 机器学习 (ML) 提供了改善高频回收预测的潜力.
研究的目的:
- 系统地审查基于ML的模型的质量和显著因素,以预测30天的高频再接收.
- 识别常见的ML方法和关键预测变量.
主要方法:
- 对2013年至2023年间发表的研究进行系统审查.
- 包括13项具有大量患者队列的研究 (1,778272,778).
- 选择研究的质量评估.
主要成果:
- 随机森林和极端梯度增强是常见的ML方法.
- 30天HF再接收率有很大的差异 (1.2%39.4%).
- 模型性能 (AUC) 从0.51到0.93.9不等.
- 在9个类别中确定了60个重要的预测因素.
结论:
- ML模型显示了预测30天HF再接收的潜力.
- 鉴定的预测因素可以为医疗保健专业人员的个性化护理规划提供信息.
- 需要进一步研究前性研究和各种数据,以提高ML模型的准确性.
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