相关实验视频
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Mechanical Ventilation Boot Camp Curriculum
Published on: March 12, 2018
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基于机器学习的机械通风持续时间的预测,包括药物数据
Andrea Sikora1, Bokai Zhao2, Yanlei Kong3
1University of Georgia College of Pharmacy, Department of Clinical and Administrative Pharmacy, Augusta, GA, USA.
medRxiv : the preprint server for health sciences
|October 4, 2023
概括
药物复杂性会影响重症患者的机械通风持续时间. 包括药物数据在预测模型中,适度提高了预测长期机械通风的准确性.
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
- 药物经济学 药物经济学
背景情况:
- 机械通风的持续时间与不良结果和重症监护中的资源利用有关.
- 药物治疗方案的复杂性与死亡率和停留时间的增加有关,但在机械通风患者中没有专门研究.
研究的目的:
- 开发机械通风持续时间的预测模型.
- 测试是否结合药物数据可以提高预测长期机械通风的模型性能.
主要方法:
- 对机械通风的成年人进行回顾性队列研究>24小时.
- 收集的数据包括患者特征,疾病严重程度得分和药物治疗方案复杂性 (MRC-ICU).
- 开发了使用后勤回归和机器学习 (XGBoost,随机森林,SVM) 进行长期机械通风 (>5天) 的预测模型.
主要成果:
- 最强的物流模型 (基础+MRC-ICU) 实现了0.72.7的AUROC.
- 随机森林模型表现出最高的性能,AUROC为0.78.
- 疾病严重程度和药物数据被确定为机器学习模型中的关键预测因素.
结论:
- 药物治疗方案的复杂性与长时间的机械通风有关.
- 结合药物信息的预测模型在预测机械通风持续时间方面提供了适度的改进.
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