机器学习预测在ICU停留期间侵入性和非侵入性通风的总持续时间
Emma Schwager1, Xinggang Liu2, Mohsen Nabian1
1Philips, Cambridge, Massachusetts, United States of America.
新模型使用ICU数据准确预测侵入性和非侵入性机械通风持续时间. 这可以通过更准确地预测患者需求来改善患者护理和资源管理.
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
- 医疗保健中的机器学习
背景情况:
- 准确预测机械通风持续时间对于管理重症监护室 (ICU) 的资源和患者的治疗结果至关重要.
- 现有的模型经常预测总通风时间,而不区分侵入性和非侵入性模式,限制了它们的临床实用性.
研究的目的:
- 开发和验证侵入性和非侵入性机械通风持续时间的独立预测模型.
- 将拟议模型的性能与已建立的基准比较,如APACHE.
主要方法:
- 利用梯度增强回归来创建两个不同的模型来预测侵入性和非侵入性通风持续时间.
- 训练有素的模型在一个大型数据集上,包括来自350家美国医院的260万名患者住院 (2010-2019).
- 进行特征重要性分析,以确定每个通风类型的关键预测因素.
主要成果:
- 这些模型的平均绝对误差 (MAE) 为侵入性通风2.08天,非侵入性通风0.36天.
- 综合预测模型的表现优于APACHE的黄金标准,MAE为2.38天而不是3.02天.
- 侵入性通风的关键预测因素包括高心率,呼吸道感染和非OR入院;对于非侵入性,更高的呼吸率和GCS测量是显著的.
结论:
- 梯度增强模型为侵入性和非侵入性通风持续时间提供了准确和独特的预测.
- 这些模型为ICU资源分配,对呼吸机引起的肺损伤的风险评估以及优化患者的氧化提供了宝贵的工具.
- 特性重要性分析揭示了影响通风持续时间的关键临床因素,有助于临床决策.
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