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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

52
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
52
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

62
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...
62
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

134
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
134
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

308
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
308
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

106
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,...
106
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

84
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...
84

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

Comments on Xu et al. (2026) "Development of a microscopy-based diagnostic test for alkali injury-induced limbal stem cell deficiency through autofluorescence multispectral imaging" (Exp. Eye Res. 264:110829).

Experimental eye research·2026
Same author

Interpretable integration of SEM and SVM for reliable thyroid nodule classification.

Artificial intelligence in medicine·2026
Same author

Reconsidering regression-based predictors in adults with attention‑deficit/hyperactivity disorder and substance use disorders.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology·2026
Same author

Beyond Parametric Assumptions: Reevaluation of Environmental Interactions in Prenatal Exposure Studies.

Chest·2026
Same author

Correspondence on "Clinical correlation between metabolic biomarkers and chemoresistance in gestational trophoblastic neoplasia" by Kong et al.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society·2026
Same author

Evaluating Linear Parametric Poisson Regression vs Nonparametric Unsupervised Learning in Ulcerative Colitis Data.

Gastroenterology·2026

相关实验视频

Updated: May 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

超越SHAP:用于临床预测模型的可靠特征选择方法.

Yoshiyasu Takefuji1

  • 1Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-ku, Tokyo 135-8181, Japan.

Archives of gerontology and geriatrics
|May 3, 2025
PubMed
概括

在临床预测中模型依赖特征的重要性是不可靠的. 这项研究引入了一个强大的框架,使用统计和信息理论方法来获得准确的临床模型见解.

科学领域:

  • 临床预测建模临床预测建模
  • 机器学习可以解释机器学习的解释性.
  • 生物统计学 生物统计学

背景情况:

  • 在临床预测中依赖模型的特征重要性方法缺乏验证,导致不可靠的见解.
  • 算法选择显著影响特征排名,即使具有可比的预测准确性.
  • 现有的验证侧重于准确性,忽视了特征重要性可靠性的关键方面.
关键词:
临床预测建模临床预测建模功能重要性验证的验证.信息理论是信息理论.模型不可知论的方法.单调的关系是单调的关系.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599
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

8.1K

相关实验视频

Last Updated: May 9, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

599
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

8.1K