Jove
Visualize
联系我们

相关概念视频

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

179
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
179

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

AI-induced never-skilling in medical education.

Nature medicine·2026
Same author

The detectability paradox: bilingual medical report generation with open-weight models and the limits of human oversight.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics.

NAR genomics and bioinformatics·2026
Same author

PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI era.

Nature medicine·2026
Same author

Enhancing Team Science by Training Collaborative Biostatisticians to have a Strong Statistical Voice.

Journal of statistical theory and practice·2025
Same author

Feasibility of a Specialized Large Language Model for Postgraduate Medical Examination Preparation: Single-Center Proof-Of-Concept Study.

JMIR formative research·2025
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

相关实验视频

Updated: Jun 28, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

31.4K

使用机器学习比较开放式数据库和传统的重症监护研究:图书识别分析研究

Yuhe Ke1, Rui Yang2, Nan Liu2

  • 1Division of Anesthesiology and Perioperative Medicine, Singapore General Hospital, Singapore, Singapore.

Journal of medical Internet research
|April 17, 2024
PubMed
概括
此摘要是机器生成的。

机器学习分析显示,开放式访问数据库 (OAD) 通过专注于预测建模来补充传统的重症监护室 (ICU) 研究. 整合OAD和传统的重症监护研究为ICU洞察提供了一个全面的未来.

关键词:
贝尔主题 贝尔主题 贝尔主题仿真 (MIMIC) 是一种模仿方式.医疗信息中心为重症监护提供医疗信息.关键护理关键护理的关键护理电信中心 (eICU)机器学习是机器学习.自然语言处理自然语言处理.

更多相关视频

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.0K
Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
10:38

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies

Published on: January 16, 2019

20.2K

相关实验视频

Last Updated: Jun 28, 2025

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
05:02

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Published on: October 24, 2019

31.4K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.0K
Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies
10:38

Observational Study Protocol for Repeated Clinical Examination and Critical Care Ultrasonography Within the Simple Intensive Care Studies

Published on: January 16, 2019

20.2K

科学领域:

  • 密集护理医学是密集护理的医学.
  • 图书统计学 图书统计学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 传统的重症监护室 (ICU) 研究主要使用随机对照试验.
  • 开放式访问数据库 (OAD) 已成为当代研究的重要资源.
  • 机器学习 (ML) 便于在大量研究中进行趋势分析.

研究的目的:

  • 进行文献分析,比较传统重症监护 (TIC) 研究中的研究趋势和主题与使用OADs的研究.
  • 利用ML进行这两种研究方法的全面比较.

主要方法:

  • 利用 ML 来分析来自 Web of Science 数据库的出版物.
  • 将研究分类为OAD和ICT组,OAD包括MIMIC,eICU-CRD,阿姆斯特丹UMCdb,HiRID和儿科重症监护数据库.
  • 采用统一的多重近似和投影来实现语料库可视化,以及BERTopic用于主题识别和分类.

主要成果:

  • 分析了145,426个ICT文章和1,301个OAD文章.
  • 信息通信技术研究显示呈指数级增长,在2021年达到顶峰,而OAD研究自2018年以来稳步增加.
  • 败血症,通风研究和儿科重症监护是突出的话题;ICT研究涵盖了更广泛的研究范围.

结论:

  • 通过专注于预测模型,OAD研究增强了传统的重症监护研究.
  • 信息和通信技术研究提供了重要的定性数据,补充了OAD的发现.
  • 整合OAD和传统的重症监护方法,以及自然语言处理,代表了ICU研究和文献审查的未来.