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

Causality in Epidemiology01:21

Causality in Epidemiology

463
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...
463
Introduction to Epidemiology01:26

Introduction to Epidemiology

772
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,...
772
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

363
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
363
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

264
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
264
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

349
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:  
349
Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

333
The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
333

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

Reducing Risk Misinformation and Miscommunication: A Sheaf-Theoretic Perspective.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Necessary conditions for valid causal inference from observational data.

Critical reviews in toxicology·2026
Same author

Integrating Fragmented Risk Knowledge: Sheaf Theory for Risk Analysts.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Combining Diverse Expert Opinions in Risk Analysis Using Relative Causal Knowledge.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Improving the design of epidemiology studies that use biomonitoring for exposure assessment: a SciPinion panel recommendation.

BMC medical research methodology·2026
Same author

Living with risk, then and now: A dual review of Cam Grey's Living with Risk in the Late Roman World and of current AI-assisted book reviewing.

Risk analysis : an official publication of the Society for Risk Analysis·2025

相关实验视频

Updated: Jul 18, 2025

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.6K

走向实际的因果流行病学.

Louis Anthony Cox1

  • 1University of Colorado School of Business and Cox Associates, 503 N. Franklin Street, Denver, CO 80218, USA.

Global epidemiology
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

人口归因分数 (PAF) 将关联与因果关系混为一谈,导致有缺陷的健康风险分析. 因果人工智能 (CAI) 为使用因果机制预测干预效应提供了一个强大的替代方案.

关键词:
有因果的人工智能.因果关系是因果关系.人口可归因的分数.因果关系的可能性.风险分析 风险分析统计方法 统计方法

更多相关视频

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

6.0K

相关实验视频

Last Updated: Jul 18, 2025

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field
10:49

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Published on: March 16, 2019

8.6K
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

6.0K

科学领域:

  • 流行病学 流行病学
  • 因果推理因果推理
  • 人工智能的人工智能

背景情况:

  • 传统的流行病学方法,如人口可归因分数 (PAF),往往将关联与因果关系混为一谈.
  • 这种混杂可能导致不准确的预测和无效的健康风险管理策略.
  • 现有的方法在复杂的数据问题上扎,例如未观察到的变量和测量错误.

研究的目的:

  • 引入因果人工智能 (CAI) 作为流行病学计算的高级框架.
  • 突出基于关联的风险归因在公共卫生中的局限性.
  • 倡导采用CAI来进行更准确的因果预测.

主要方法:

  • 总结了因果人工智能 (CAI) 方法的发展.
  • 讨论CAI对不完美和复杂数据集的应用.
  • 使用因果机制的定量描述,如条件概率表和结构方程.

主要成果:

  • CAI方法提供了一个框架,通过建模因果机制来预测干预措施的影响.
  • CAI可以解决未观察到的变量,缺失的数据和测量错误所带来的挑战.
  • 该研究表明,CAI有可能提高健康风险评估的准确性.

结论:

  • 因果人工智能 (CAI) 提供了一种比传统方法更严格的流行病学分析方法.
  • 将基于关联的风险因素替换为因果预测,提高了健康风险评估的实际价值.
  • CAI为更有效的公共卫生干预和风险管理提供了基础.