机器学习模型的开发,用于预测断奶结果,仅基于自发呼吸试验期间的连续通风器参数
Ji Eun Park1, Do Young Kim2, Ji Won Park1
1Department of Pulmonary and Critical Care Medicine, Ajou University School of Medicine, Suwon 16499, Republic of Korea.
Bioengineering (Basel, Switzerland)
|October 28, 2023
概括
一个新的机器学习模型预测机械通风断奶成功,仅使用呼吸试验期间的通风器数据. 这种工具有助于临床医生实时做出输出管的决定,从而有可能改善患者的治疗结果.
科学领域:
- 关键护理医学 关键护理医学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 停止机械通风是一个复杂的临床挑战.
- 准确预测患者准备进行输出管的情况对于优化护理和减少并发症至关重要.
- 目前的方法可能无法充分利用自发呼吸试验 (SBT) 中可用的连续监测数据.
研究的目的:
- 开发和验证一种机器学习模型,用于预测机械通风断奶的结果.
- 在SBT期间利用来自通风机的持续监测参数作为预测模型的输入.
- 评估模型的性能,并为临床决策提供可解释的见解.
主要方法:
- 开发了一个基于卷积神经网络 (CNN) 的模型来分析各种长度的数据,包括三种波形和25个数值参数,这些数据来自SBTs期间的呼吸机.
- 使用了医疗重症监护室138名患者的数据,随机分为8:2进行培训和测试.
- 用梯度加权类激活映射 (Grad-CAM) 来实现模型的可解释性.
主要成果:
- 该模型实现了0.912的接收器操作特征曲线 (AUC-ROC) 下的面积,以预测断奶成功.
- 精度回忆曲线下的面积 (AUC-PR) 为0.767,表明性能强.
- 格拉德-CAM突出了有影响力的特征,为模型的预测提供了视觉解释.
结论:
- 开发的机器学习模型使用现有的SBT数据准确预测机械通风断奶结果.
- 该模型提供了一个可解释的工具,以帮助临床医生实时地做出输出管的决定.
- 这种方法有可能通过促进及时停止呼吸机治疗来改善患者的治疗结果.
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