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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

135
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
135
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

382
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
382
Causality in Epidemiology01:21

Causality in Epidemiology

436
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
436
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

582
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...
582
Introduction to Epidemiology01:26

Introduction to Epidemiology

747
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
747
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

306
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:  
306

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

Associations of proteomic age clocks with lifestyle risk factors, incident chronic diseases and mortality in two European cohorts.

Nature aging·2026
Same author

Embrace Open, Collaborative, Discovery-Based Exposomics.

Environmental science & technology·2026
Same author

Corrigendum to "Environmental exposure and cancer incidence in offshore petroleum workers in Norway" [Environ. Res. 264 (2025) 121407].

Environmental research·2026
Same author

Reliability of interrater occupation coding and potential impact on occupational exposure assessment.

Annals of work exposures and health·2026
Same author

REPLY TO: "Daylight Saving Time and Mortality-Proceed with Caution "in response to "Daylight saving time affects European mortality patterns" by Levy et al.

Nature communications·2026
Same author

Mitochondrial DNA breaks and copy number and the risk of lung cancer in the Shanghai Women's Health Study.

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology·2026

相关实验视频

Updated: Jul 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

人工智能在流行病学工作编码方面超过了人类.

Mathijs A Langezaal1,2, Egon L van den Broek3, Susan Peters4

  • 1Population-Based Epidemiological Cohorts Unit UMS11, INSERM, 16 Avenue Paul Vaillant Couturier, Paris, 94807, Villejuif, France. m.a.langezaal@uu.nl.

Communications medicine
|November 5, 2023
PubMed
概括
此摘要是机器生成的。

通过准确地分类职位描述以进行暴露评估,OPERAS提高了职业健康. 与手动方法相比,这种自动化系统显著减少了工作量,并提高了准确性.

更多相关视频

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

596
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

241

相关实验视频

Last Updated: Jul 11, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

596
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

241

科学领域:

  • 职业健康和安全 职业健康和安全
  • 流行病学 流行病学
  • 计算语言学计算语言学

背景情况:

  • 工作环境对员工的健康有重大影响.
  • 在职业群体中准确的暴露评估依赖于编码职位描述.
  • 现有的自动编码工具往往需要人类干预以获得精确度.

研究的目的:

  • 开发OPERAS,用于流行病学职位编码的决策支持系统.
  • 提高分类自由文本职位描述的准确性和效率.
  • 为了能够对大型职业队伍进行可靠的暴露评估.

主要方法:

  • 开发了OPERAS,这是一个可定制的决策支持系统,用于工作编码.
  • 在PCS2003,NAF2008,ISCO-88和ISCO-68.8的812,522个职位条目上训练有素的分类模型.
  • 使用工作暴露矩阵 (甲,,ALOHA,DOM) 评估模型准确性.

主要成果:

  • 实现了0.66-0.84的编码器间可靠性 (科恩卡帕),超过了专家编码器 (0.59-0.76).
  • 暴露评估的准确性在75.0-98.4%之间.
  • 在编码过程中,估计至少减少了19.7-55.7%的工作量.

结论:

  • 在职业分类和暴露评估中,OPERAS确保了高准确度.
  • 该系统大大降低了工作量,并且性能优于专家编码器和当前工具.
  • 能够进行大规模,高效和有效的暴露评估,以获得更健康的工作环境.