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精确预测低血糖和高血糖使用机器学习在危急病患者

Yu Ikeda¹, Satoru Sugimoto², Tetsuo Ishikawa^2,3,4,5

¹Department of Emergency and Critical Care Medicine, Chiba University Graduate School of Medicine, 1-8-1 Inohana, Chuo, Chiba, 260-8677, Japan.

Scientific reports

|December 18, 2025

概括

机器学习准确地预测了在重症监护室 (ICU) 患者的低血糖和高血糖,提前6小时. 这种早期检测工具有助于积极管理重症监护中的葡萄糖异常.

科学领域:

关键护理医学关键护理医学
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

低血糖和高血糖是重症患者常见的,具有挑战性的并发症.
维持正常的血糖水平至关重要,但由于许多因素而困难.
早期发现葡萄糖失调对于有效的患者管理至关重要.

研究的目的:

开发和验证一种机器学习模型,用于预测重症监护室 (ICU) 患者的低血糖和高血糖症,提前六小时.
使用例行收集的ICU数据评估各种机器学习算法的预测性能.

主要方法:

从8853名ICU患者 (超过130万条记录) 的电子健康记录分析,从2010年到2022年.
使用 eXtreme Gradient Boosting (XGBoost),随机森林,神经网络和物流回归的预测模型的开发.
利用人口,生理,实验室和治疗变量进行模型培训和验证.

主要成果:

在预测低血糖 (AUC:0.939,F1:0.520) 和高血糖 (AUC:0.919,F1:0.702) 方面,XGBoost模型获得了最高的性能.
模型显示了高校准度和净效益,表明可靠的预测.
机器学习算法准确地预测了重症患者的葡萄糖异常.

结论:

机器学习模型,特别是XGBoost,显示出在ICU患者中准确预测葡萄糖异常的巨大潜力.
这种预测能力支持在重症监护机构早期发现和主动管理失糖症.
这些发现表明,这是一种有价值的工具,可以提高重症监护室患者的治疗结果.

关键词:

危急病患者的病情非常严重.过高血糖症的发生.低血糖症是一种低血糖症.重症监护病房 (ICU) 是一个重症监护病房.机器学习机器学习预测模型的预测模型.

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