使用机器学习模型,确定在重症监护病人的感染相关咨询的需要
Leslie R Zwerwer1,2, Christian F Luz3, Dimitrios Soudis4
1Department of Health Sciences, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands. l.r.zwerwer@rug.nl.
Scientific reports
|January 28, 2024
概括
机器学习模型可以主动识别需要感染咨询的重症监护室 (ICU) 患者. 这种使用电子健康记录的方法可以提前八小时预测咨询,从而改善患者的护理和结果.
科学领域:
- 医疗信息学 医疗信息学
- 关键护理医学 关键护理医学
- 机器学习 机器学习
背景情况:
- 在重症监护室 (ICU) 进行与感染相关的咨询可以提高护理质量和患者的治疗结果.
- 目前的咨询实践是反应性和事件触发的,可能会推迟关键干预.
研究的目的:
- 开发和评估一种主动的机器学习方法,用于识别需要感染相关咨询的ICU患者.
- 利用常规的电子健康记录进行早期预测,而不依赖于传统的触发器,如微生物学报告.
主要方法:
- 利用一个学术三级医院的混合ICU的9684次入院的数据.
- 逻辑回归,随机森林,梯度增强机器和长短期记忆 (LSTM) 神经网络的比较预测性能.
- 专注于使用常规电子健康记录的时间敏感建模.
主要成果:
- 长期短期记忆 (LSTM) 模型实现了AUROC为0.921和AUPRC为0.541,可以提前8小时预测咨询.
- 时间敏感的建模方法优于静态方法.
- 在不使用微生物学报告等常规触发器的情况下实现了成功的预测.
结论:
- 使用机器学习和电子健康记录,可以积极预测ICU中与感染相关的咨询.
- 这种方法可以简化ICU团队和顾问的工作流程.
- 早期识别有潜力提高护理质量,并改善疑似感染的重症患者的治疗结果.
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