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在全身麻醉患者中,术后康复的预测效果,使用基于群集智能机器学习的可解释模型

Chenqiao Hua¹, Yeyuan Chu¹, Minshu Zhou¹

¹Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Frontiers in physiology

|September 15, 2025

概括

这项研究开发了一种可解释的机器学习模型,用于预测全身麻醉后的术后恢复. 该模型准确地识别了可能出现延迟恢复的患者,有助于临床决策.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
麻醉学麻醉学

背景情况:

术后恢复对于患者的治疗结果和医院资源管理至关重要.
预测恢复可以优化患者护理途径并减少并发症.
当前的预测方法可能缺乏准确性或可解释性.

研究的目的:

开发和验证可解释的群集智能机器学习模型,用于预测在接受全身麻醉的患者中术后恢复.
确定与手术后恢复不良相关的关键临床变量.
评估预测模型的临床实用性.

主要方法:

从1,128名接受全身麻醉的患者中回顾性收集临床数据.
使用群集智能机器学习 (XGBoost基础学习器) 开发预测模型.
使用内部 (测试集) 和外部 (数据集B) 数据集验证模型.

主要成果:

拉索回归确定了七个关键预测因素:手术持续时间,麻醉持续时间,中性粒细胞与淋巴细胞的比率 (NLR),C反应蛋白 (CRP),血清肌,体重指数 (BMI) 和年龄.
该模型实现了高性能,在训练组中F1得分为0.8447,AUC为0.9265,在外部验证中AUC为0.8383.
该模型显示了术后恢复的显著预测能力.

结论:

一个可解释的群集智能机器学习模型有效地预测了全身麻醉后患者的术后恢复.
该模型的预测可以帮助临床医生识别有风险的患者并规划干预措施.
这种方法增强了临床决策,改善了患者的治疗结果.

关键词:

一般麻醉全身麻醉可以解释的解释性.术后康复术后康复预测预测预测预测群众智能机器学习机器学习视觉化系统可视化系统

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