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Updated: Jun 22, 2025

A Data-Driven Approach to Quantifying Immune States in Sepsis
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使用预测性深度学习模型,优化针对败血症患者的个性化能量传递.

Lu Wang1,2, Li Chang3, Ruipeng Zhang1,2

  • 1Institute for Emergency and Disaster Medicine, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Asia Pacific journal of clinical nutrition
|July 5, 2024
PubMed
概括
此摘要是机器生成的。

优化使用深度学习模型为败血症患者提供能量至关重要. 只有在早期急性阶段才建议允许营养不足,以后增加摄入量以改善生存率.

关键词:
深度学习是一种深度学习.提供能源的能源交付.机器学习是机器学习.营养支持营养支持这是一种血症.

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科学领域:

  • 关键护理医学 关键护理医学
  • 营养支持 营养支持
  • 医疗保健中的人工智能

背景情况:

  • 败血症管理需要精确的营养支持.
  • 在重症监护室 (ICU) 的败血症患者中,个性化的能量传递至关重要.
  • 当前的能量传递策略可能无法解释毒症期间的动态代谢变化.

研究的目的:

  • 开发和验证深度学习模型,以优化成人败血症患者的个性化能量传递.
  • 在毒症的不同代谢阶段中确定最佳的能量目标.

主要方法:

  • 这是一项对179名在ICU的成年败血症患者进行的回顾性研究,在14天内收集了47个指标.
  • 数据分为三个代谢阶段:急性早期,急性晚期和康复.
  • 为每个阶段的最佳能源目标建立深度学习模型,并进行外部验证.

主要成果:

  • 确定最佳能量目标为900 kcal/d (早期急性),2300 kcal/d (晚急性) 和2000 kcal/d (康复性).
  • 早期急性阶段的过度能量和晚期急性阶段的不足能量增加了死亡率.
  • 在康复阶段,过度和不足的能量输送都与较高的死亡风险有关.

结论:

  • 时间序列深度学习模型可以优化ICU中败血症患者的能量传递.
  • 只有在急性早期阶段才建议允许营养不良.
  • 在后期阶段增加能量摄入量可能会提高生存率并解决能源赤字.