机器学习模型的开发和验证,用于儿童重症社区获得性肺炎的临界进展风险
Xiaoqian Ma1, Wu Zhao2, Qi Sun1
1Department of Pediatrics, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
Scientific reports
|December 2, 2025
概括
机器学习准确地预测了儿童严重社区获得性肺炎 (SCAP) 的进展到临界SCAP (cSCAP). 关键预测因素包括前素和乳酸脱酶,有助于早期风险分层.
科学领域:
- 儿科重症监护医药 儿科重症监护医药
- 机器学习在医疗保健中的应用.
- 对传染病的预测建模.
背景情况:
- 在儿童中,严重的社区性肺炎 (SCAP) 具有严重的SCAP (cSCAP) 进展风险.
- 早期识别患有cSCAP高风险的儿童对于及时干预和改善结果至关重要.
- 现有的预测工具可能缺乏有效的临床决策所需的准确性和特异性.
研究的目的:
- 开发和验证基于机器学习的预测模型,用于儿童患者SCAP向cSCAP的进展.
- 确定最能预测cSCAP发展的关键临床变量.
- 加强早期风险分层和临床决策支持,以管理儿科SCAP.
主要方法:
- 来自211名小儿SCAP患者的临床数据的回顾性分析.
- 使用后勤回归 (LR) 和LASSO进行变量选择.
- 开发并比较了七个机器学习模型 (LR,DT,RF,XGBoost,NB,KNN,SVM) 进行预测.
- 采用了SHAP分析来分析模型的可解释性.
主要成果:
- 极端梯度增强 (XGBoost) 模型表现出卓越的性能,AUC为0.98.
- 发现的关键预测因子包括前素 (PCT),乳酸脱酶 (LDH),红细胞分布宽度变化系数 (RDW-CV) 和血尿素 (BUN).
- 该XGBoost模型实现了高精度 (0.89),灵敏度 (0.98) 和特异性 (0.75).
结论:
- 一个准确的机器学习模型,特别是XGBoost,可以有效地预测儿科SCAP向cSCAP的进展.
- PCT,LDH,RDW-CV和BUN是儿童cSCAP早期风险分层的重要指标.
- 开发的模型为医疗保健提供者在管理儿科SCAP时提供了有价值的临床支持.
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