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开发和验证用于预测剖腹产后疼痛和个性化疼痛管理策略的机器学习模型:一个多中心研究

Shenjuan Lv¹, Ning Sun², Chunhui Hao³

¹Department of Anesthesiology, Jinan Second Maternal and Child Health Hospital, Shandong, China.

BMC anesthesiology

|April 11, 2025

概括

机器学习模型可以个性化剖腹产后的术后疼痛管理. XGBoost模型显示出最好的结果,确定胺和麻醉类型是改善患者结果的关键因素.

科学领域:

产科和妇科产科和妇科
麻醉学麻醉学
医疗信息学医疗信息学

背景情况:

剖腹产后的术后疼痛管理由于个体变化而存在挑战.
标准化的协议往往不足,突出了个性化策略的需要.

研究的目的:

使用机器学习开发用于剖腹产的个性化术后疼痛管理协议.
确定疼痛管理结果的关键预测因素.

主要方法:

评估了八种机器学习模型,包括XGBoost,随机森林和神经网络,基于来自两个医院队伍的数据.
使用RMSE和R2等指标进行内部和外部验证.
采用SHAP价值分析来确定重要的预测因素.

主要成果:

该XGBoost模型实现了卓越的性能,具有最低RMSE和最高R2.
标明了使用sketamine,麻醉方法和麻醉药物类型作为关键因素.
素显著延迟了患者控制的静脉静脉止痛 (PCIA) 激活.

结论:

机器学习提供了一种有前途的方法来完善产科护理中的术后疼痛管理.
个性化的方案,包括诸如胺和特定麻醉方法等因素,可以提高患者的治疗效果.

关键词:

艾斯凯特胺是什么意思机器学习是机器学习.疼痛管理疼痛管理在剖腹产后的疼痛.在SHAP中,SHAP值是指SHAP值.

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