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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.2K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

626
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
626
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Classification of Illness01:17

Classification of Illness

8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

531
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...
531

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

The financial consequences of undiagnosed memory disorders.

Journal of financial economics·2025
Same author

Nonclinical factors associated with the treatment of older women with newly diagnosed low-grade ductal carcinoma in situ.

Cancer·2023
Same author

Barriers and Strategies Used to Continue School-Based Health Services During the COVID-19 Pandemic.

Maternal and child health journal·2023
Same author

Adherence to hormonal therapy after surgery among older women with ductal carcinoma in situ: Implications for breast cancer-related adverse health events.

Cancer·2023
Same author

Development and Assessment of a Social Media-Based Construct of Firearm Ownership: Computational Derivation and Benchmark Comparison.

Journal of medical Internet research·2023
Same author

Assessing Social Media Data as a Resource for Firearm Research: Analysis of Tweets Pertaining to Firearm Deaths.

Journal of medical Internet research·2022

相关实验视频

Updated: Jan 7, 2026

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

7.9K

使用代表性不足的子组微调来提高疾病预测的公平性.

Yanchen Wang1, Rex Bone1, Will Fleisher1

  • 1Georgetown University, Washington, DC, U.S.A.

Biomedical engineering systems and technologies, international joint conference, BIOSTEC ... revised selected papers. BIOSTEC (Conference)
|December 31, 2025
PubMed
概括
此摘要是机器生成的。

医疗保健中的人工智能需要公平. 一种新的微调方法改善了疾病预测模型,即使数据不平衡,也提高了对所有患者群体的公平性.

关键词:
疾病预测 疾病预测机器学习公平性 机器学习公平性模型 微调 微调多变量敏感属性多变量敏感属性

更多相关视频

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

866
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

7.9K

相关实验视频

Last Updated: Jan 7, 2026

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

7.9K
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
03:08

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

866
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

7.9K

科学领域:

  • 医疗保健人工智能的人工智能
  • 机器学习伦理学 机器学习伦理学

背景情况:

  • 人工智能 (AI) 越来越多地用于医疗保健中的疾病预测.
  • 由于人口差异,人们对人工智能模型的透明度,问责制和公平性存在担忧.
  • 有限的研究解决了改善模型公平性的问题,特别是在多变量敏感属性和倾斜的组分布方面.

研究的目的:

  • 探索预测心脏病和阿尔茨海默病及相关痴呆症 (ADRD) 的算法公平性.
  • 提出和评估一种新的微调方法,以提高预测模型中的公平性.

主要方法:

  • 开发了一种微调方法,使用从多数组数据中预先训练的模型.
  • 用代表性不足的子组的数据对模型进行了微调,以纳入特定知识.
  • 评估了该方法的表现与其他公平性设定方法相比.

主要成果:

  • 提议的微调方法在所有子组中都超过了现有的方法.
  • 即使在高度不平衡的子组分布和非常小的子组中,也证明了有效性.
  • 该方法成功地结合了子组特定的知识.

结论:

  • 微调方法是改善疾病预测中的AI模型公平性的有希望的方法.
  • 这项工作有助于开发更公平的医疗保健人工智能工具,解决预测建模中的差异.
  • 对提高公平性的技术进行进一步的研究对于公平的医疗保健AI至关重要.