基于新型图形的集中和分散方法用于早期AKI预测
V S Suresh Kumar1, R Devi Priya2, M Vijayakumar3
1Department of Computer Science and Engineering, Nandha College of Technology, Erode, Tamil Nadu, India. thepegaasus@gmail.com.
Scientific reports
|December 6, 2025
概括
这项研究引入了新的基于图形的模型,用于早期检测急性损伤 (AKI). 这些方法可以提前几小时准确预测AKI的发病情况,从而改善患者的治疗结果和医疗保健中的隐私.
科学领域:
- 人工智能在医学中的应用
- 机器学习用于医疗保健
- 图形神经网络用于预测分析.
背景情况:
- 急性损伤 (AKI) 对住院患者构成重大威胁,需要及早检测以减轻严重后果.
- 传统的预测模型在分散式系统中难以处理复杂的生理数据和数据隐私.
- 基于图形的方法为分析复杂的时间序列生理数据提供了一个有希望的途径.
研究的目的:
- 开发和评估两种基于图形的新型模型,一种集中式图形注意网络 (GAT) 和一种分散式聊学习与自适应聚合GAT (GL-AA-GAT),用于早期AKI检测.
- 用生理时间序列数据提前6-12小时预测AKI发作.
- 评估这些模型的性能和通用性,特别是保护隐私的去中心化方法.
主要方法:
- 使用Kaggle Sepsis数据集,一个生理时间序列数据集.
- 开发了一个集中的图形注意网络 (GAT),使用多头注意来进行特征交互建模.
- 创建了一个去中心化的GL-AA-GAT模型,使用八交换和适应性聚合来保护隐私,在多个节点上进行可扩展的培训.
主要成果:
- 集中式GAT实现了94.1%的准确性,94%的灵敏度,95%的AUC-ROC和91%的AUPRC.
- 分散的GL-AA-GAT表现出强的性能,92.8%的准确性,93%的灵敏性,93.8%的AUC-ROC和90%的AUPRC,同时确保了隐私.
- 两种模型的表现都超过了现有方法,表明在预测视野和外部验证队列中具有很高的预测可靠性和稳定性.
结论:
- 开发的基于图形的模型,GAT和GL-AA-GAT,提供高度可靠和准确的急性损伤的早期检测.
- 分散的GL-AA-GAT模型为分布式临床环境提供了一个创新的,保护隐私的,灵活的解决方案.
- 这些先进的AI方法具有显著的潜力,可以改善患者护理和重症监护机构的结果.
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