在重症患者中使用机器学习对加速计和止痛药数据进行日常疼痛分类.
Jessica Sena1, Sabyasachi Bandyopadhyay2,3, Mohammad Tahsin Mostafiz2
1Federal University of Minas Gerais/Department of Computer Science, Belo Horizonte, Brazil.
IEEE International Conference on Bioinformatics and Biomedicine workshops. IEEE International Conference on Bioinformatics and Biomedicine
|March 11, 2024
概括
可穿戴的加速度计可以使用机器学习预测重症监护室 (ICU) 患者的疼痛. 这项技术有助于识别疼痛事件,并可以通过患者数据和药物信息来增强.
科学领域:
- 生物医学工程 生物医学工程
- 关键护理医学 关键护理医学
- 机器学习应用 机器学习应用
背景情况:
- 由于沟通障碍,重症监护室 (ICU) 的疼痛评估是很困难的.
- 之前的研究表明,身体活动和重症患者的疼痛之间存在联系.
研究的目的:
- 开发机器学习模型,使用加速度计数据来预测ICU患者的疼痛水平.
- 评估可穿戴传感器在客观疼痛量化中的有效性.
主要方法:
- 训练后勤回归,CatBoost和XG-Boost模型在加速度计数据,患者人口统计和疼痛史上.
- 将疼痛分为"疼痛与无疼痛"和严重程度等级,分别为白天和夜晚.
- 利用SHAP分析来识别关键的预测特征.
主要成果:
- 机器学习模型准确地预测了疼痛事件,CatBoost显示出更高的夜间性能 (AUC:0.82).
- 加速度计特征是最重要的,但人口统计学和药物也发挥了作用.
- 包括止痛药的数据改善了疼痛严重程度的分类.
结论:
- 加速度计数据,患者人口统计和疼痛史可以有效地选ICU中的疼痛发作.
- 这种方法,加上药物数据,可以对疼痛严重程度进行中等分类.
- 可穿戴式传感器技术显示出在重症监护中的客观疼痛监测的前景.
关键词:
加速度计的速度计.在Actigraph中使用Actigraph.在 CatBoost 中使用 CatBoost.在ICU中,医生会对患者进行治疗.重症监护病房的重症监护病房.机器学习 机器学习闪闪发光的闪闪发光的人更多相关视频
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