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

488
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:
488

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

Rh(III)-Catalyzed C-H Activation/[3 + 2] Annulation of <i>N</i>-Phenoxyacetamides via Carbooxygenation of 1,3-Dienes.

Organic letters·2021
Same author

Structure-guided engineering of adenine base editor with minimized RNA off-targeting activity.

Nature communications·2021
Same author

Changes in the age-specific body mass index distribution among urban children between 2002 and 2018 in Changsha, China.

Translational pediatrics·2021
Same author

Bonding the Terminal Isocyanate-Related Functional Group to the Surface Manganese Ions to Enhance Li-Rich Cathode's Cycling Stability.

ACS applied materials & interfaces·2021
Same author

Association between Sleep Duration, Physical Activity, and Mental Health Disorders: A Secondary Analysis of the National Survey of Children's Health 2017-2018.

BioMed research international·2021
Same author

Ogt controls neural stem/progenitor cell pool and adult neurogenesis through modulating Notch signaling.

Cell reports·2021
Same journal

A meta-analysis of the effects of Baduanjin training on the human body temperature based on infrared thermography technology.

Medicine·2026
Same journal

The predictive ability of "TyG_CVAI" for incident stroke in individuals with different glycemic metabolic status: A national cohort study.

Medicine·2026
Same journal

Symptoms and quality of life in gynecological cancer patients after surgery: Application of latent profile and network analysis.

Medicine·2026
Same journal

Massive hemoptysis as the initial presentation of Behçet disease complicated by multisite thromboembolism: A case report.

Medicine·2026
Same journal

Dextromethorphan-bupropion-associated pharmacovigilance signals based on the FAERS database: An observational study.

Medicine·2026
Same journal

Effects of Mulligan sustained natural apophyseal glide mobilizations on pain, mobility, and lumbar-related disability in chronic non-specific low back pain: A systematic review and meta-analysis.

Medicine·2026
查看所有相关文章

相关实验视频

Updated: Jan 15, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
06:01

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

Published on: August 18, 2015

15.4K

一个基于可解释机器学习的中风风险预测模型.

Qinggui Li1, Xiao Wang2, Qian Ye1

  • 1Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.

Medicine
|October 7, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型有效预测中风风险. 随机森林算法分析了运动频率和小牛周长等因素,显示出高精度,有助于临床决策.

关键词:
机器学习是机器学习.身体活动 身体活动预测模型 预测模型一次性中风中风中风中风中风

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

相关实验视频

Last Updated: Jan 15, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
06:01

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

Published on: August 18, 2015

15.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习
  • 心血管疾病预测预测

背景情况:

  • 在全球范围内,中风是导致死亡和残疾的主要原因.
  • 准确预测中风风险对于及时干预和预防至关重要.
  • 现有的风险评估工具可能无法完全捕捉中风病因的复杂性.

研究的目的:

  • 开发和评估用于预测中风风险的机器学习模型.
  • 确定导致中风发病率的关键风险因素.
  • 评估各种算法在中风风险预测中的性能.

主要方法:

  • 对134名中风患者和354名对照患者的回顾性分析.
  • 开发和比较八个机器学习模型,包括随机森林 (RF).
  • 使用LASSO和后勤回归进行特征选择;通过ROC/PR曲线和准确度指标进行模型评估.

主要成果:

  • 射频算法实现了高性能:ROC AUC为0.96,PR AUC为0.92,特异性为0.97,精度为0.92.
  • 确定的主要预测因素包括每周运动日,小腿周长,病史,性别,BMI和STRATIFY分数.
  • 沙普利补充解释证实了体力活动和人类测量对中风风险的重大影响.

结论:

  • 机器学习,特别是射频算法,为中风风险预测提供了强大的方法.
  • 定期的体力活动和特定的生理标记是中风风险的重要决定因素.
  • 这些预测模型可能会增强临床决策和个性化中风预防策略.