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

102
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:
102
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38

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

Exploring the Antimicrobial Efficacy of Mesenchymal Stem Cell Secretome Against Klebsiella pneumoniae in Urinary Tract Infections.

Clinical and translational science·2026
Same author

Optimized extraction of polyphenols from rooibos tea (<i>Aspalathus linearis</i>) and their biological activities.

Frontiers in nutrition·2026
Same author

Valorization of Onion By-Products and Assessment of Their Biological Activities.

Foods (Basel, Switzerland)·2026
Same author

Prevalence and antimicrobial resistance of Acinetobacter baumannii isolates at Lebanese Hospital Geitaoui-University Medical Center: A Five-year study (2017-2021).

International journal of medical microbiology : IJMM·2026
Same author

AI-Driven Assistive Technology: The Disability Justice Imperative.

Studies in health technology and informatics·2026
Same author

Advancing Psychiatric Safety With the Predictive Risk Identification for Mental Health Events Tool: Retrospective Cohort Study.

JMIR mental health·2026

相关实验视频

Updated: May 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

评估机器学习异常检测算法的实用方法 流行病预警系统的异常检测算法

Antoine Saab1,2, Abdul Hamid Dabboussi3, Cynthia Abi Khalil1,4

  • 1Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire de recherche en informatique pour la santé, Limics, F-75006 Paris, France.

Studies in health technology and informatics
|May 17, 2025
PubMed
概括

机器学习模型显示出疫情监测早期预警系统 (EWS) 的前景. 这项研究开发了一种用于传染病监测的机器学习的实用评估方法,其表现优于传统方法.

关键词:
异常检测检测异常检测早期预警系统 早期预警系统流行病监测 流行病监测 流行病监测机器学习 机器学习流行病的准备情况.

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
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

7.4K

相关实验视频

Last Updated: May 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
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

7.4K

科学领域:

  • 流行病学 流行病学
  • 计算机科学 计算机科学
  • 公共卫生 公共卫生

背景情况:

  • 流行病监测的传统统计和基于规则的方法 早期预警系统 (EWS) 难以处理动态数据,需要频繁的专家调整.
  • 机器学习 (ML) 在处理复杂,多维数据和适应不断变化的模式以改善流行病监测方面提供了优势.
  • 在适应和评估ML模型与传染病监测中的黄金标准数据的实际方法中存在差距.

研究的目的:

  • 提出一种基于ML的异常检测在流行病监测中的实用评估方法.
  • 使用一组统计模型进行验证,建立一个黄金标准数据集.
  • 为了验证LSTM和隔离森林ML模型的性能与现实世界的病原体数据相比.

主要方法:

  • 结合四种传统统计模型的合奏技术被用来创建一个黄金标准数据集.
  • 两种机器学习模型,长短期记忆 (LSTM) 和隔离森林,用于异常检测.
  • 模型的性能使用四种病原体的时间序列数据进行了验证:COVID-19,C型肝炎,Acinetobacter baumannii和耐美西林黄金葡萄球菌.

主要成果:

  • 该研究报告了对LSTM和隔离森林模型与已建立的黄金标准数据集进行验证的有希望的结果.
  • 机器学习模型在检测各种传染病数据系列中的异常方面表现出有效性.
  • 开发的评估方法为评估流行病监测中的ML算法提供了实际框架.

结论:

  • 这些发现支持对ML算法的调整,以加强流行病监测和早期预警系统 (EWS).
  • 实用的评估方法和黄金标准数据集对于传染病监测的未来研究和开发非常有价值.
  • 学习的经验可以指导ML的整合,以实现更强大,更适应的公共卫生监测.