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

Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Variability: Analysis01:11

Variability: Analysis

137
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
137
Bias01:22

Bias

4.1K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
4.1K
Randomized Experiments01:13

Randomized Experiments

6.9K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
6.9K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

125
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
125
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K

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

Cardiometabolic multimorbidity and survival after out-of-hospital cardiac arrest.

Resuscitation plus·2026
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

Uncertainty and unmet needs in older high-risk blunt trauma survivors and their caregivers: a multi-centre mixed methods study.

Scientific reports·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

相关实验视频

Updated: Jun 21, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K

用可解释机器学习进行变量重要性分析,用于公平风险预测.

Yilin Ning1, Siqi Li1, Yih Yng Ng2,3

  • 1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore.

PLOS digital health
|July 12, 2024
PubMed
概括

沙普利变量重要性云 (ShapleyVIC) 提供了一种可靠和可解释的方法来评估机器学习中的变量重要性. 这种方法通过可靠地识别关键因素并正式测试其意义,提高了临床风险预测.

更多相关视频

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K

相关实验视频

Last Updated: Jun 21, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.1K

科学领域:

  • 临床信息学是一种临床信息学.
  • 机器学习 机器学习
  • 统计建模 统计建模

背景情况:

  • 机器学习 (ML) 方法被广泛用于变量重要性评估.
  • 然而,传统的"黑子"ML模型往往缺乏稳定性,样本大小小小,并没有正式识别非重要的变量.
  • 这限制了它们在临床风险预测等关键应用中的可靠性和解释性.

研究的目的:

  • 引入和评估Shapley变量重要性云 (ShapleyVIC) 作为一种新的方法,用于可靠和可解释的变量重要性评估.
  • 解决现有的ML方法的局限性,特别是在样本规模有限的场景中,需要进行正式的显著性测试.
  • 评估ShapleyVIC在提高临床风险预测模型的公平性和准确性方面的潜力.

主要方法:

  • 沙普利VIC使用一组回归模型来评估变量的重要性.
  • 这种整体方法提高了模型的稳定性和可解释性.
  • 该方法包括不确定性估计,以正式测试变量重要性的意义.

主要成果:

  • 沙普利VIC在一项临床研究中成功确定了重要的变量,而随机森林和XGBoost模型失败了.
  • 该方法通过复制来自较小子样本的结果来证明其稳定性,即使统计能力下降.
  • 莎普利VIC正确地将种族确定为无意义的,支持将其排除在预测模型之外,并与传统的逐步方法形成对比.

结论:

  • 沙普利VIC为机器学习中的变量重要性评估提供了一个强大的和可解释的解决方案.
  • 它能够正式测试意义和处理有限数据的能力提高了可靠性.
  • 沙普利VIC具有重要的潜力,可以为更公平,更准确的临床风险预测做出贡献.