在接受家庭机械通风的患者中预测呼吸系统损失补偿:机器学习模型开发和验证研究
Nerea Berbel Casado1, Francesc López Seguí1,2, Natalia Muñoz Moruno1
1Chair in ICT and Health, Centre for Health and Social Care Research (CESS), Universitat de Vic - Universitat Central de Catalunya, Carrer Miquel Martí i Pol, 1, Vic, Spain.
JMIR formative research
|December 30, 2025
概括
机器学习模型可以使用家用机械通风 (HMV) 远程监控数据预测患者的呼吸不补偿. 随机森林模型显示出平衡的性能,突出了早期干预和改善患者治疗结果的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 呼吸系统医学 呼吸系统医学
背景情况:
- 慢性呼吸道疾病需要长期的家庭机械通风 (HMV).
- 远程监测HMV患者提供实时数据,但可能会错过早期脱补偿迹象.
- 不被注意到的恶化导致紧急访问和住院,增加医疗保健负担.
研究的目的:
- 开发和评估一种机器学习模型,用于预测HMV患者的呼吸失补偿事件.
- 利用远程监控数据来识别急性恶化的早期迹象.
- 通过及时进行医疗干预来改善患者的结果.
主要方法:
- 在三个远程监控平台上对482名HMV患者的数据进行了回顾性分析.
- 数据包括设备的使用,遵守,口罩泄漏和呼吸机设置在事件发生前5周的窗口.
- 训练和评估多个机器学习模型,优先考虑回忆,使用10倍交叉验证和SHAP分析进行解释性.
主要成果:
- 后勤回归实现了高回忆率 (0.94),但准确度中等 (0.60).
- 随机森林分类器提供了最好的平衡:准确性 (0.66),回忆 (0.78),和F1得分 (0.70).
- 在SHAP分析中,增加的设备使用,泄漏和合规性被确定为脱补偿的关键预测因素.
结论:
- 使用HMV远程监控数据预测呼吸失补偿是可行的.
- 基于树木的模型,如随机森林,显示出平衡性能和临床见解的前景.
- 需要进一步研究更大的多中心数据集和额外的生理数据,以提高模型的稳定性.
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