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使用机器学习模型预测重症监护室急性脑损伤患者的医院肺炎

Junchen Pan¹, Zhen Yue², Jing Ji²

¹Department of Neurosurgery, The Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Frontiers in medicine

|April 28, 2025

概括

机器学习模型准确地预测了急性脑损伤的重症监护室 (ICU) 患者的医院肺炎. XGBoost在识别高风险患者方面表现出卓越的表现,有助于早期干预.

科学领域:

医疗信息学医疗信息学
关键护理医学关键护理医学
机器学习机器学习

背景情况:

鼻腔肺炎是ICU急性脑损伤患者的重大风险.
预测这种感染对于及时干预和改善患者结果至关重要.

研究的目的:

开发和验证机器学习模型,用于预测急性脑损伤的ICU患者的医院肺炎.
确定与肺炎风险相关的关键临床变量.

主要方法:

对280名急性脑损伤的ICU患者的回顾性审查.
开发和验证XGBoost,随机森林和其他机器学习模型.
使用准确度,精度,回忆,F1得分和AUC进行评估.

主要成果:

在训练组中,XGBoost显示了最高的AUC (0.956).
随机森林和自适应提升在验证组中实现了最高的AUC (0.883).
关键预测因素包括气管切割时间,抗生素使用,血糖,通风时间和C反应蛋白.

结论:

机器学习模型有效地预测了这种患者群体的医院肺炎风险.
开发的模型显示了可靠的预测性能,可用于临床应用.

关键词:

严重的脑损伤是急性脑损伤.在曲线下面的面积.重症监护病房的重症监护病房是一个重症监护病房.机器学习模型的机器学习模型.在医院的肺炎.

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