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

Nursing Clinical Information System01:27

Nursing Clinical Information System

1.2K
Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
1.2K

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

Evaluating Artificial Intelligence for Sepsis Prediction in Emergency Departments: A Systematic Review and Meta Analysis.

Journal of medical systems·2026
Same author

Data-Driven Guideline Adherence in Data Representation and Compliance Measurement: Scoping Review.

Journal of medical Internet research·2026
Same author

White Matter Structure in Complex Regional Pain Syndrome: A High Angular Resolution and Fixel-Based Study.

European journal of pain (London, England)·2025
Same author

Using Electronic Health Data to Deliver an Adaptive Online Learning Solution to Emergency Trainees: Mixed Methods Pilot Study.

JMIR medical education·2025
Same author

Implementation evaluation of an evidence-informed hospital inpatient nursing framework (HIRAID® Inpatient): a protocol for a stepped-wedge cluster RCT.

Trials·2025
Same author

Where Are the Greatest Risks for Choosing Unwisely? A Survey of Emergency Department Clinicians.

Emergency medicine Australasia : EMA·2025
Same journal

Machine learning-based prediction of dataset-defined myocardial infarction risk: A retrospective computational study for precision cardiovascular risk assessment.

Digital health·2026
Same journal

Visualization of artificial intelligence applications in oral disease diagnosis: A bibliometric analysis.

Digital health·2026
Same journal

Wearable IoT health sensing beyond functional utility: Identity-expressive and hedonic determinants of user acceptance in intimate physiological monitoring devices - A sequential FA-ANP mixed-methods investigation with digital health policy implications.

Digital health·2026
Same journal

Heterogeneity of information across seven curated national or international digital health app repositories.

Digital health·2026
Same journal

Interpretable machine learning for early prediction of acute kidney injury in critically ill patients with acute pancreatitis.

Digital health·2026
Same journal

Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.

Digital health·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 2026

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

469

解码败血症:一个算法驱动系统架构的技术蓝图.

Abdullah Safi1, Mostafa Shaikh2, Minh Trang Hoang3

  • 1NSW Ministry of Health, Sydney, Australia.

Digital health
|November 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个无服务器机器学习架构,用于紧急部门的快速败血症风险分层. 该系统实现了高精度,使得败血症患者能够及时干预.

关键词:
败血症 这是一种败血症.人工智能的人工智能是人工智能.紧急情况部门的急救部门.机器学习是机器学习.无服务器云是无服务器的云.系统架构 系统架构

更多相关视频

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

10.5K
Cecal Ligation Puncture Procedure
11:53

Cecal Ligation Puncture Procedure

Published on: May 7, 2011

56.1K

相关实验视频

Last Updated: Jan 11, 2026

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

469
Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression
07:30

Design of Cecal Ligation and Puncture and Intranasal Infection Dual Model of Sepsis-Induced Immunosuppression

Published on: June 15, 2019

10.5K
Cecal Ligation Puncture Procedure
11:53

Cecal Ligation Puncture Procedure

Published on: May 7, 2011

56.1K

科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习操作 机器学习操作
  • 医疗保健系统工程 系统工程

背景情况:

  • 败血症是一个关键的医疗保健挑战,死亡率高.
  • 早期检测和干预对于改善患者的治疗结果至关重要.
  • 目前的方法往往缺乏实时功能,特别是在急诊室 (ED) 设置中.

研究的目的:

  • 提出一个可扩展的,无服务器机器学习 (ML) 操作架构,用于近实时的败血症风险分层.
  • 通过在急救等待室识别高风险患者,实现及时的败血症治疗.
  • 克服ED环境中无法获得的病理学数据的局限性.

主要方法:

  • 在亚马逊网络服务 (AWS) 中通过MuleSoft集成HL7消息处理.
  • 使用AWS Lambda进行实时数据处理和AWS SageMaker进行ML模型部署.
  • 开发一种极端梯度增强模型,使用不同年龄组的接收器操作特征 (ROC) 曲线进行评估.

主要成果:

  • 该系统以99.7%的HL7消息处理成功率显示出高可靠性.
  • 极端梯度提升模型实现了0.84的整体精度和0.80.8的F1得分.
  • 跨年龄队列观察到强的表现,曲线下的面积 (AUC) 值在0.806到0.867.7之间.

结论:

  • 开发的架构为ED等候室近乎实时的败血症风险分层提供了强大的解决方案.
  • 该系统有可能显著提高早期败血症检测和干预的潜力.
  • 对峰值负载场景和代码集管理进行进一步的优化.