Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

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

40 Hz biparietal transcranial alternating current stimulation for Alzheimer's disease: a prospective study.

BMC medicine·2026

Same author

GhTTLL12 Coordinates With Transcriptional Regulators GhMML3 and GhMYB86 to Orchestrate Cotton Fibre Development Through Modulating Microtubule Dynamics.

Plant biotechnology journal·2026

Same author

Omentin-1 Val109Asp polymorphism and increased coronary artery disease risk in smokers with type 2 diabetes.

Medicine·2026

Same author

An Autocatalytic Molecular Sensor Enables Rapid, Sensitive, and One-Pot Detection of Nucleic Acids.

ACS nano·2026

Same author

Piezo1-activated mesenchymal stem cells-derived extracellular matrix hydrogel promotes the repair of osteoporotic bone defects through osteogenic and angiogenic coupling.

Regenerative biomaterials·2026

Same author

Research Progress of La<sub>1-x</sub>Sr<sub>x</sub>MnO<sub>3</sub>-Based Flexible Wearable Sensors.

Micromachines·2026

Search research articles

相关实验视频

Updated: May 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

基于机器学习,为老年败血症患者构建早期预警模型.

Xuejie Ma¹, Yaoqiong Mai^1,2, Yin Ma¹

¹Intensive Care Unit, Cardiocerebral Vascular Disease Hospital, General Hospital of Ningxia Medical University, Yinchuan, 750003, Ningxia Hui Autonomous Region, China.

Scientific reports

|March 28, 2025

概括

这项研究开发了一个AI模型来预测老年患者的败血症. XGBoost模型实现了高精度,确定了基线APTT和淋巴细胞计数作为早期毒症检测的关键风险因素.

关键词:

早期预警模型的早期预警模型.机器学习 (ML) 是指机器学习.败血症这是一种败血症.在XGBoost中使用.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

相关实验视频

Last Updated: May 5, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

A Data-Driven Approach to Quantifying Immune States in Sepsis

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

科学领域:

医疗信息学医疗信息学
老年学是一门学科.
关键护理医学关键护理医学

背景情况:

败血症构成重大威胁,特别是对老年人来说.
早期识别高风险老年患者对于及时干预至关重要.
人工智能为开发早期预警系统提供了有希望的功能.

研究的目的:

开发和评估基于机器学习的早期预警模型,用于老年患者的败血症.
在这个人口群体中确定预测败血症的关键临床特征.
利用人工智能在老年人群中改善败血症预测.

主要方法:

利用了来自2976名接受紧急和重症监护单位治疗的老年患者的临床数据.
选了12种临床特征,并使用了8种机器学习模型进行预测.
使用XGBoost算法开发了一个早期预警模型.

主要成果:

该XGBoost模型表现出高性能,AUROC为0.971,精度为0.95.95.
确定的关键预测因素是基线激活部分血栓形成时间 (APTT) 和基线淋巴细胞计数.
基线APTT升高和基线淋巴细胞数量降低与败血症风险增加有关.

结论:

成功开发了一种高性能机器学习模型,用于在老年人中预测早期的败血症.
该模型特别突出了APTT和淋巴细胞计数,可以帮助早期启动治疗.
建议进行进一步的外部验证,以确认模型的通用性.