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

Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K
Residual Plots01:07

Residual Plots

4.5K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.5K
Longitudinal Research02:20

Longitudinal Research

11.8K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
11.8K

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

Transcriptomic and metabolomic analysis of autumn leaf color change in <i>Fraxinus angustifolia</i>.

PeerJ·2023
Same author

Chitosan-Linked Dual-Sulfonate COF Nanosheet Proton Exchange Membrane with High Robustness and Conductivity.

Small (Weinheim an der Bergstrasse, Germany)·2023
Same author

Pulmonary Artery Denervation Inhibits Left Stellate Ganglion Stimulation-Induced Ventricular Arrhythmias Originating From the RVOT.

JACC. Clinical electrophysiology·2023
Same author

High-Temperature-Induced Pore System Evolution of Immature Shale with Different Total Organic Carbon Contents.

ACS omega·2023
Same author

Frequency Domain Filtering Method for SSVEP-EEG Preprocessing.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2023
Same author

CCL12 induces trabecular bone loss by stimulating RANKL production in BMSCs during acute lung injury.

Experimental & molecular medicine·2023

相关实验视频

Updated: May 29, 2025

Treating Low Back Pain in Failed Back Surgery Patients with Multicolumn-lead Spinal Cord Stimulation
04:42

Treating Low Back Pain in Failed Back Surgery Patients with Multicolumn-lead Spinal Cord Stimulation

Published on: June 26, 2018

14.5K

一个多维回归模型用于预测慢性腰部疼痛的复发.

Yilong Huang1,2,3,4, Chunli Li4, Jiaxin Chen4

  • 1Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Sciences, Guangzhou, China.

European journal of pain (London, England)
|February 4, 2025
PubMed
概括

预测慢性腰痛复发现在更加准确. 一个新的多维机器学习模型 (MDM) 在识别高风险患者方面超过了STarT BACK工具 (SBT).

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

416

相关实验视频

Last Updated: May 29, 2025

Treating Low Back Pain in Failed Back Surgery Patients with Multicolumn-lead Spinal Cord Stimulation
04:42

Treating Low Back Pain in Failed Back Surgery Patients with Multicolumn-lead Spinal Cord Stimulation

Published on: June 26, 2018

14.5K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.6K
Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
06:28

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

416

科学领域:

  • 生物医学工程 生物医学工程
  • 在医疗保健中的数据科学.
  • 临床流行病学临床流行病学

背景情况:

  • 慢性腰部疼痛 (CLBP) 经常复发,这构成了重大的临床挑战.
  • 准确预测CLBP复发对于有效的管理和预防策略至关重要.
  • 现有的工具在预测长期复发风险方面存在局限性.

研究的目的:

  • 开发和验证用于预测CLBP复发的机器学习工具.
  • 将一个新的多维模型 (MDM) 与STarT BACK工具 (SBT) 的性能进行比较.
  • 确定与CLBP复发相关的关键临床因素.

主要方法:

  • 对341名CLBP患者进行前性队列研究.
  • 基于多变量逻辑回归 (MRL) 的多维模型 (MDM) 的开发和内部验证.
  • 使用AUC,灵敏度和特异性,将MDM性能与START BACK工具 (SBT) 的性能进行比较.

主要成果:

  • 在2年内,在38.42%的患者中观察到复发.
  • MDM的AUC达到0.813,灵敏度为85.2%,特异性为70.2%.
  • SBT的性能显著降低,AUC为0.555,灵敏度为93.3%,特异性为17.6%.

结论:

  • 开发的MDM有效预测了CLBP患者2年的复发风险.
  • 与SBT相比,MDM表现出优越的预测性能.
  • 这种模型可以帮助临床医生识别高风险个体,以进行有针对性的预防干预.