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Updated: Feb 25, 2026

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A Data-Driven Approach to Quantifying Immune States in Sepsis
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一个多式嵌入模型用于败血症数据表示
Tuo Liu1, Yonglin Li2,3,4, Hongyi Chen1
1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
NPJ digital medicine
|February 23, 2026
概括
一个新的败血症数据表示模型 (SepsisDRM) 集成了表格和临床笔记中的患者数据. 这种败血症研究模型有效地预测了结果,并对患者进行了分层,改善了败血症护理.
科学领域:
- 生物医学信息学 生物医学信息学
- 临床数据科学 临床数据科学
- 人工智能在医学中的应用
背景情况:
- 败血症研究面临挑战,原因是有限的标记数据和仅专注于表格输入的模型.
- 现有的模型往往忽略了临床文本中的丰富信息,阻碍了全面的患者理解.
研究的目的:
- 引入败血症数据表示模型 (SepsisDRM),这是一个专为败血症研究而设计的创新嵌入模型.
- 开发一种能够共同处理表格和文本患者数据的模型,以增强表现.
- 通过整合各种数据源来克服现有的败血症模型的局限性.
主要方法:
- 开发了SepsisDRM,这是一个嵌入模型,在19,526名败血症患者的大数据集上进行训练.
- 该模型共同处理表格数据和临床文本,以创建全面的患者表示.
- 评估了SepsisDRM在各种与败血症相关的任务中的概括性,而无需对特定任务进行微调.
主要成果:
- 败血症DRM在各种败血症相关任务中表现出强大的概括能力.
- 该模型成功地将患者分为四种临床上可解释的表型.
- 在28天结果预测方面获得了高AUC分数:0.92 (回顾),0.94 (前性) 和0.78 (外部数据集).
结论:
- 败血症DRM是第一个专门为败血症研究开发的嵌入模型.
- 通过整合表格和文本信息,为败血症数据分析建立了一个新的范式.
- 为败血症研究和潜在的其他研究领域提供了有前途的方法,这些研究领域需要多式联运数据集成.
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