Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Steps in Outbreak Investigation

Steps in Outbreak Investigation

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:

Statistical Methods for Analyzing Epidemiological Data

Statistical Methods for Analyzing Epidemiological Data

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:

Principles of Disease Surveillance

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Causality in Epidemiology

Causality in Epidemiology

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...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

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:

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

Producing concept-motivated signs supports learning of STEM in American Sign Language.

NPJ science of learning·2026

Same author

Development and Evaluation of SNOMED CT Automated Mapping Tool: Advancing Terminology Standardization and Semantic Interoperability.

JMIR medical informatics·2026

Same author

Index-Tapered Nanostructured Films via Selective Porosification for Enhanced Light Harvesting and Environmental Robustness.

ACS applied materials & interfaces·2026

Same author

Laser-Architected Shape-Configurable Vertical Graphene Thermoacoustic Loudspeakers for 3D Acoustic Emission.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Simulation-Based Training in Intraosseous Access: Improving Procedural Skills in Pediatric Residents.

Sage open pediatrics·2025

Same author

IFNγ signaling drives resistance to FLT3 inhibition in acute myeloid leukemia.

iScience·2025

Same journal

Real-World Implementation of Large Language Models for Writing Clinical Discharge Summaries Within a Secure Data Environment: Development and Expert Evaluation Study.

JMIR AI·2026

Same journal

Prediction of Type 2 Diabetes Mellitus From Chest X-Rays Using a Suite of Previously Developed Chronic Disease Deep Learning Models in an Ethnically Diverse Cohort: Observational Study.

JMIR AI·2026

Same journal

Ambient AI Scribes and Emergency Department Documentation Burden: Retrospective Cohort Study.

JMIR AI·2026

Same journal

Supporting Radiology Resident Education and Clinical Decision-Making With Large Language Models: Comparative Study of Reasoning Models DeepSeek-R1 and ChatGPT-o1.

JMIR AI·2026

Same journal

Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study.

JMIR AI·2026

Same journal

Application of Language Models for the Analysis of Adverse Drug Events in Pharmaceutical Research and Development: Scoping Review.

JMIR AI·2026

查看所有相关文章

Search research articles

相关实验视频

Updated: Mar 13, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

通过流行病学和移动性特征增强COVID-19查模型:机器学习模型研究

Hyunwoo Choo¹, Dohyung Lee², Soo-Yong Shin¹

¹Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.

|March 11, 2026

概括

此摘要是机器生成的。

用于COVID-19查的机器学习模型通过将移动性和流行病数据与症状信息相结合而显著改进. 这种增强的方法可以提高传染病的诊断准确度.

关键词:

在 COVID-19 疫情中,深度学习是一种深度学习.流行病学流行病学机器学习是机器学习.群众选群众选群众选群众选移动性数据的移动性数据

更多相关视频

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

相关实验视频

Last Updated: Mar 13, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

科学领域:

流行病学流行病学
机器学习机器学习
公共卫生公共卫生

背景情况:

在COVID-19流行后,使用症状数据和机器学习 (ML) 进行患者查的研究激增.
关于患者轨迹和流行病学状况的关键数据在这些ML模型中仍未得到充分利用.
现有的COVID-19查ML模型往往缺乏全面的数据集成.

研究的目的:

为了提高ML模型的性能,用于COVID-19查.
将患者的症状数据与移动性和流行病信息相结合.
通过数据丰富,提高传染病诊断的准确性.

主要方法:

通过智能手机应用程序从48,798名个人收集了每日自我报告的症状,位置和测试结果.
结合应用程序数据与我们的世界数据和国家流行病信息.
训练了五个ML模型 (逻辑回归,XGBoost,LightGBM,TabNet,Google AutoML) 来分类COVID-19感染状态.

主要成果:

整合流动性和流行病数据显著改善了所有五种ML模型的性能.
通过添加外部数据,接收器运行特征曲线 (AUC) 下的面积从0.8712增加到0.9104.
外部数据源显然提高了ML模型用于疾病查的性能.

结论:

移动性和流行病数据,结合症状数据,可以显著提高ML模型的准确性,用于COVID-19诊断.
结合上下文信息可以提高对COVID-19等传染病的查能力.
这种方法为公共卫生监测和患者查提供了更强大的方法.