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

Introduction to z Scores01:06

Introduction to z Scores

9.7K
A z score (or standardized value) is measured in units of the standard deviation. It tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a zero z score. It is important to note that the mean of the z scores is zero, and the standard deviation is one.
z scores...
9.7K
Ethical Standards I01:25

Ethical Standards I

845
The American Nurses Association (ANA) created and implemented the first nationally accepted Code of Ethics for Nurses with Interpretive Statements. The Code of Ethics is a living document regularly updated by the ANA and establishes an ethical standard that is non-negotiable for nurses in all roles and settings.
The Code of Ethics provisions outline the nurse's duty to the patient, the healthcare team, the profession, and society. The Code's fundamental principles include advocacy,...
845
Ethical Standards II01:23

Ethical Standards II

703
Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
Nurses are entrusted with upholding various ethical principles and standards. Nurses forge solid therapeutic relationships using trust, empathy, autonomy, confidentiality, and professional competence.
Confidentiality is crucial, embodying respect for individual privacy...
703
Confidence Coefficient01:24

Confidence Coefficient

7.7K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.7K
Legal Guidelines for Documentation01:06

Legal Guidelines for Documentation

1.3K
The legal guidelines for nursing documentation are essential for ensuring accurate, professional, and ethical recording of patient care. The guidelines are discussed here:
1.3K
Survey Safety01:28

Survey Safety

51
Surveying near highways, rough terrain, or power lines involves significant risks. Working along highways is particularly dangerous and requires the use of warning signs and flagmen. It is safest to avoid working directly on roads and use offsets whenever possible. When highway work is unavoidable, it must follow all safety guidelines. Surveyors should wear bright clothing, such as orange reflective vests, to ensure visibility to motorists, coworkers, and hunters. In construction zones, wearing...
51

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

Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.

Journal of clinical and translational science·2026
Same author

A SEQUENTIAL SIGNIFICANCE TEST FOR TREATMENT BY COVARIATE INTERACTIONS.

Statistica Sinica·2026
Same author

Strategies for mitigating artificial intelligence bias in healthcare: a systematic review.

JAMIA open·2026
Same author

Defining Prenatal Care Surveillance Metrics Using Electronic Health Record Data.

JAMA health forum·2026
Same author

Cardiovascular Disease Risk and Noncardiovascular Chronic Disease Burden by Housing Status.

Journal of the American Heart Association·2026
Same author

Multinational validation of the PREVENT and SCORE2 cardiovascular risk equations across 6.4 million individuals.

Nature medicine·2026

相关实验视频

Updated: Jul 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

618

联邦得分 (FedScore):一个保护隐私的框架,用于开发联合得分系统.

Siqi Li1, Yilin Ning1, Marcus Eng Hock Ong2

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

Journal of biomedical informatics
|September 3, 2023
PubMed
概括

联邦得分 (FedScore) 允许保护隐私的联合学习,用于在多个机构中生成准确的得分系统. 这一框架证明了协作研究的良好通用性和稳定性.

关键词:
临床决策 临床决策分布式算法 分布式算法电子健康记录电子健康记录电子医疗记录电子医疗记录联合学习是联合学习.评分系统 评分系统

更多相关视频

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.1K

相关实验视频

Last Updated: Jul 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

618
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
06:18

The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm

Published on: October 20, 2022

2.1K

科学领域:

  • 联邦学习学习 (Federated Learning) 是一种学习方式.
  • 医疗信息学 医疗信息学
  • 合作研究合作研究.

背景情况:

  • 跨机构的合作对于开发强大的评分系统至关重要.
  • 现有的方法经常面临数据隐私和信息隔离方面的挑战.

研究的目的:

  • 介绍FedScore,一个新的保护隐私的联合学习框架.
  • 促进在多个站点生成分数系统,以加强协作.

主要方法:

  • 费德斯科尔包括五个模块:联合变量排名,转换,得分导出,模型选择和评估.
  • 使用10个模拟站点开发了一个用于死亡率预测的假设全球评分系统.
  • 绩效与本地和集中评分系统进行了比较.

主要成果:

  • 联邦调查局的FedScore模型实现了曲线下的平均面积 (AUC) 为0.763 (SD 0.020).
  • 费德斯科尔 (FedScore) 显示出有希望的准确性和稳定性,接近于聚合模型的表现.
  • 它的标准偏差低于大多数本地生成的模型.

结论:

  • 费德斯科尔是一个可行的隐私保护工具,用于生成评分系统.
  • 该框架显示了在多地点研究中具有良好的概括性的潜力.