在临床试验中优先选择患者:用于动态预测ICU入院患者在医院死亡率的机器学习算法,使用重复测量数据
Emma Pedarzani1, Alberto Fogangolo2, Ileana Baldi3
1Clinical Trial and Biostatistics, Research and Innovation Unit, University Hospital of Ferrara, 44124 Ferrara, Italy.
Journal of clinical medicine
|January 25, 2025
概括
一个新的机器学习模型,MixRFb,使用红细胞分布宽度 (RDW) 和年龄准确预测重症监护室 (ICU) 的死亡率,改善了临床试验患者的选择.
科学领域:
- 关键护理医学 关键护理医学
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 临床试验的重症监护室 (ICU) 患者选择面临挑战.
- 像SAPS这样的现有评分系统在预测死亡率方面存在局限性.
- 红细胞分布宽度 (RDW) 显示出作为死亡率预测因素的潜力.
研究的目的:
- 开发和评估一个机器学习预后评分系统,用于ICU死亡率.
- 评估新算法的性能与现有方法相比.
- 确定ICU死亡率的关键预测因素,以改善患者分层.
主要方法:
- 开发了一种混合效应的物流随机森林对二进制数据 (MixRFb) 算法.
- 综合随机森林 (RF) 分类与混合效应模型.
- 将MixRFb性能与基于RF和SAPS的评分进行比较,使用接收器运行特征曲线.
主要成果:
- 与基于SAPS的评分 (0.814) 相比,MixRFb在曲线下的面积 (0.882) 更高.
- 年龄和RDW被确定为ICU死亡率的最重要的预测因素.
- 该算法在预测住院死亡率方面表现出卓越的有效性.
结论:
- 混合RFb算法可以更好地预测ICU死亡率.
- 年龄和RDW是预测ICU患者死亡率的关键因素.
- 这种工具可以增强临床试验的患者选择,改善结果和道德.
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