开发和验证机器学习模型,以预测第二天的输出
Samuel W Fenske1, Alec Peltekian2, Mengjia Kang1
1Division of Pulmonary and Critical Care, Northwestern University Feinberg School of Medicine, Chicago, USA.
Scientific reports
|July 30, 2025
概括
机器学习模型可以预测ICU患者第二天的输出管准备情况. 这种决策支持工具可以通过比目前的方法更早地识别输出管的机会来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
- 生物医学信息学 生物医学信息学
- 医疗保健中的机器学习
背景情况:
- 机械通风 (MV) 释放标准通常不准确,导致长时间的MV或再输管.
- 不良结果与长时间的MV和再输卵有关.
- 协议驱动的每日评估加速了输出管,但需要专门的员工.
研究的目的:
- 确定应用到电子健康记录 (EHR) 的机器学习 (ML) 是否可以预测第二天的输出管.
- 评估ML模型在预测输出管准备的性能.
主要方法:
- 检查了37个临床特征从12 AM-8 AM在ICU天从一个潜在的队列.
- 使用了三种数据编码/归算策略.
- 构建并比较XGBoost,LightGBM,后勤回归,LSTM和RNN模型进行预测.
- 在内部和外部ICU队列上测试模型.
主要成果:
- 最好的模型 (LSTM) 在内部和外部测试队列中实现了0.870的AUROC.
- 关键预测因素包括平原压力和里士满兴奋镇静量表 (RASS) 评分.
- 模型通常在实际输出输出前几天预测输出准备 (63.8%在3天内).
结论:
- 机械通风模型显示出作为机械通风释放临床决策支持工具的前景.
- ML模型可能有助于更早地识别准备用于输出管的患者.
- 需要进一步的随机对照试验来确认与基于协议的护理相比的安全性,疗效和成本效益.
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