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机器学习的葡萄糖预测模型用于败血症患者
Xiang Cao1, Dong Wang2,3, Jue-Xia He2,3,4
1Department of Mathematics, University of South Carolina, Columbia, SC, 29208, USA.
Scientific reports
|November 18, 2025
概括
在败血症中准确预测葡萄糖对于重症监护至关重要. 这项研究表明,像PatchTST和DLinear这样的先进机器学习模型可以有效预测葡萄糖水平,帮助个性化患者管理.
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
- 数据科学数据科学数据科学
背景情况:
- 败血症引起的葡萄糖波动在重症监护病房 (ICU) 带来了重大挑战.
- 准确的葡萄糖监测和预测对于改善败血症患者的治疗结果至关重要.
- 患有败血症的糖尿病患者经历了复杂的血糖变化.
研究的目的:
- 评估各种先进预测模型的性能,以预测患有败血症的糖尿病患者的葡萄糖水平.
- 为了比较基于变压器的模型,一个动态线性模型和一个集成的ChatGPT-4方法用于葡萄糖预测.
- 在重症监护机构中确定短期和长期葡萄糖预测的最佳模型.
主要方法:
- 在19,621个连续血糖监测数据点上训练了多种预测模型,包括iTransformer,Crossformer,PatchTST,FEDformer和DLinear.
- 使用一个集合零射击推断方法与ChatGPT-4.
- 使用平均最大百分比误差 (MMPE) 评估模型性能,跨越15分,30分和60分钟的预测视界,并具有30分钟的回顾窗口.
主要成果:
- 在15分钟的葡萄糖预测中,PatchTST显示了最低的MMPE (3.0%).
- 在较长的时间内,DLinear实现了优异的性能,MMPEs为7.46% (30分钟) 和14.41% (60分钟).
- 整体ChatGPT-4方法也产生了具有竞争力的预测结果.
结论:
- 先进的机器学习模型,特别是DLinear和PatchTST,显示出对ICU血糖预测和管理的重大前景.
- 这些模型可以支持持续的葡萄糖监测,并为败血症患者的个性化血糖控制策略做出贡献.
- 这些发现为数字双胞胎在适应性葡萄糖管理的重症监护中实施铺平了道路.
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