揭开差距:用于未测量的离子的机器学习模型
Furkan Tontu1, Zafer Çukurova2
1Department of Anesthesiology and Reanimation, Başakşehir Çam and Sakura City Hospital, Istanbul 34480, Turkey.
Diagnostics (Basel, Switzerland)
|February 13, 2026
概括
基数过量差距 (BEGap) 有效地估计了重症患者未测量的离子,优于传统方法,如蛋白校正离子差距 (AGc) 和强离子差距 (SIG). 这为评估酸干扰提供了一个实用,床边适用的工具.
科学领域:
- 关键护理医学 关键护理医学
- 身体生理学 身体生理学
- 生物化学 生物化学
背景情况:
- 未测量的离子 (UI) 在重症患者中显著影响酸平衡.
- 目前的估计方法 (AGc,SIG,BEGap) 具有不确定的最佳效用.
- 准确的UI评估对于管理酸干扰至关重要.
研究的目的:
- 为了比较传统,斯图尔特和分区基数过剩 (BE) 方法的解释性表现,用于估计UI.
- 评估BEGap,SIG和AGc作为动脉pH的决定因素的实用性.
- 用机器学习评估分析模型的概括性.
主要方法:
- 追溯队列研究与发展 (2274名患者) 和验证 (1202名患者) 队列.
- 评估使用多变量线性回归和机器学习 (RF,XGBoost,SVR) 的传统,斯图尔特和分区BE方法.
- 通过调整的R2,RMSE,MAE和可变重要性指标 (SHAP,换) 评估模型性能.
主要成果:
- 分区式BE方法在MLR (调整后R2=0.949) 和ML分析 (R2高达0.989) 中显示出最高的解释性性能.
- 在所有模型中,BEGap始终成为动脉pH的强大,独立的决定因素,在所有模型中表现优于SIG和AGc.
- 在机器学习模型中,XGBoost表现出最稳定,最准确的性能.
结论:
- BEGap 是一种实用,生理学信息丰富,并适用于床边的参数,用于评估UI.
- 对于重症患者的线性和非线性分析模型,BEGap的表现优于AGc和SIG.
- 分区式BE方法,特别是BEGap,为UI驱动的酸干扰提供了更好的洞察力.
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