Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

105
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
105

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Discovering expert-level Nash equilibrium algorithms with large language models.

Nature communications·2026
Same author

Spermine oxidase-DOX conjugates reshape tumor microenvironment via carbonyl stress to potentiate bladder cancer chemotherapy.

Materials today. Bio·2026
Same author

Mixed exposure to brominated flame retardants adversely affects vaccine-induced antibody titers: Risk prioritization and benchmark dose modeling.

Toxicology letters·2026
Same author

Engineering of Immunoactive Hydrogels Complements Tumor-Treating Fields for Glioblastoma Therapy.

ACS nano·2026
Same author

Association between swimming pool attendance and allergic diseases: An updated systematic review and meta-analysis.

Regulatory toxicology and pharmacology : RTP·2026
Same author

Microbiological Quality of Drinking Water From On-Demand Purified Water Dispensers in Residential Communities.

Water environment research : a research publication of the Water Environment Federation·2025

相关实验视频

Updated: Jun 5, 2025

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
05:28

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Published on: December 9, 2022

3.4K

CISepsis:一种因果推理框架,用于早期检测败血症.

Qiang Li1, Dongchen Li1, He Jiao2

  • 1School of Microelectronics, Tianjin University, Tianjin, China.

Frontiers in cellular and infection microbiology
|December 16, 2024
PubMed
概括

这项研究介绍了CISepsis,一种新的早期败血症预测方法,使用因果推理去除混因素. 与现有模型相比,CISepsis显著提高了预测准确性,稳定性和可解释性.

关键词:
这就是MIMIC-IV.后门干预的后门干预有关因果推理的推理.这是一个仪器变量.这是一种血症.

更多相关视频

A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

145
Cecal Ligation Puncture Procedure
11:53

Cecal Ligation Puncture Procedure

Published on: May 7, 2011

54.7K

相关实验视频

Last Updated: Jun 5, 2025

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis
05:28

Evaluation of a Reliable Biomarker in a Cecal Ligation and Puncture-Induced Mouse Model of Sepsis

Published on: December 9, 2022

3.4K
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

145
Cecal Ligation Puncture Procedure
11:53

Cecal Ligation Puncture Procedure

Published on: May 7, 2011

54.7K

科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 因果推理因果推理

背景情况:

  • 早期败血症预测模型通常使用结构化的电子医疗记录数据.
  • 败血症涉及复杂的生理相互作用,导致混合数据类型和混因素.
  • 数据中的混因素可以掩盖真正的因果关系,降低模型的概括性和解释性.

研究的目的:

  • 开发一种早期败血症预测方法,消除混效应并捕捉因果关系.
  • 在败血症预测中增强模型的概括性,稳定性和解释性.

主要方法:

  • 提出一种因果推断方法 (CISepsis) 来识别和消除混效应.
  • 构建了一个因果结构图来分析观察,混因素和标签之间的关系.
  • 采用后门调整和仪表变量方法,优化相互信息以消除混影响.

主要成果:

  • 与XGBoost,LSTM和MGP-AttTCN相比,CISepsis在MIMIC-IV数据集上的曲线下面面积 (AUC) 显著改善.
  • 在多个预测时间点实现了高AUC值 (0.921-0.926).
  • 废弃实验证实了拟议方法的有效性.

结论:

  • 因果推断有效地消除了混因素,提高了早期败血症预测的准确性.
  • 与传统方法相比,CISepsis提供了更好的概括性,稳定性和解释性.
  • 未来的工作包括探索临床应用和干预分析的反事实调整.