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

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

94
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
94

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

Adoption of intravascular imaging use among intermediate to high-volume operators in the United States from 2019 to 2023: a Medicare data analysis.

The Journal of invasive cardiology·2026
Same author

Early Nephrology Consultation and Acute Kidney Injury in Hospitalized Patients: A Randomized Clinical Trial.

JAMA network open·2026
Same author

Stiffness-tunable oral nanoparticles facilitate damaged islet β cell restoration for diabetes treatment.

Science advances·2026
Same author

MXene-Based Optical Fiber Sensors for Chemical and Biosensing: Review and Perspectives.

Analytical chemistry·2026
Same author

Missed Opportunities for Stroke Prevention in Hypertensive Patients: A Retrospective Case-Control Study.

medRxiv : the preprint server for health sciences·2026
Same author

Development and Validation of a Traditional Chinese Medicine Constitution-Based Risk Score for Advanced Colorectal Neoplasia in Asymptomatic Chinese Adults.

Cancer management and research·2026

相关实验视频

Updated: Jun 10, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

455

随着和没有人工智能的早期预警分数.

Dana P Edelson1,2, Matthew M Churpek3, Kyle A Carey1

  • 1Section of Hospital Medicine, University of Chicago, Chicago, Illinois.

JAMA network open
|October 15, 2024
PubMed
概括

与其他AI和非AI早期预警分数相比,eCART人工智能 (AI) 评分在识别临床恶化方面表现优越. 这种人工智能工具提供了更早的检测和更少的错误报警,允许及时干预医院环境.

更多相关视频

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

相关实验视频

Last Updated: Jun 10, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

455
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K

科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的人工智能
  • 患者安全 患者安全

背景情况:

  • 早期预警分数 (EWS) 对于检测住院患者的临床恶化至关重要.
  • 现有的EWS具有不同的比较性能,需要进一步评估.

研究的目的:

  • 为了比较三个专有人工智能 (AI) EWS 与三个公开可用的简单聚合加权分数的性能.
  • 评估不同EWS在识别患者病情恶化方面提供的准确性和领先时间.

主要方法:

  • 一项回顾性队列研究包括了7家医院超过36万名成年医疗外科病房的接触.
  • 六个EWS被评估:史诗恶化指数 (EDI),罗斯曼指数 (RI),eCARTv5 (eCART),修改早期预警得分 (MEWS),国家早期预警得分 (NEWS) 和NEWS2.
  • 临床恶化被定义为转移到ICU或24小时内死亡.

主要成果:

  • eCART在接收器操作特征曲线 (0.895) 下获得了最高的面积,表明了优越的歧视.
  • 新闻2和新闻也表现强,表现优于EDI,RI和MEWS.
  • 在中等风险值时,所有得分均为至少20小时的中位数;在高风险值时,eCART提供了最长的中位数 (11小时).

结论:

  • 与其他评估得分相比,eCART AI得分显示出更高的准确性,并提供了更长的临床恶化检测时间.
  • 像NEWS这样的公开可用的分数也显示出显著的表现,超过了一些专有AI工具.
  • 调查结果表明,在EWS的开发和实施方面,需要加强透明度和监督.