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

122
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:
122

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

Pilot study of the use of ezetimibe in dogs with hyperlipidemia.

Veterinary research communications·2026
Same author

Serological progression and time to seroconversion in serodiscordant and seronegative dogs tested for visceral leishmaniasis in an endemic area of Brazil.

Acta tropica·2026
Same author

Risk and protective factors for canine visceral leishmaniasis in the Americas: a systematic review update with meta-analysis.

Parasites & vectors·2026
Same author

The Dog-Guardian Relationship and Its Meanings: Perceptions, Expectations, and Impacts on Guardians' Lives.

Animals : an open access journal from MDPI·2026
Same author

Comparative Assessment of Viral Load Retention in Surgical and Fabric Masks Worn by COVID-19 Patients.

Viruses·2025
Same author

Fifth Strategic Plan for the Development of Epidemiology in Brazil (2025-2029).

Revista brasileira de epidemiologia = Brazilian journal of epidemiology·2025

相关实验视频

Updated: Jun 24, 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

1.4K

机器学习的利用用于登革热病例查.

Bianca Conrad Bohm1, Fernando Elias de Melo Borges2, Suellen Caroline Matos Silva3

  • 1Laboratory of Veterinary Epidemiology, Postgraduate Program in Veterinary, Federal University of Pelotas (UFPel), Capão do Leão, RS, Brazil. biankabohm@hotmail.com.

BMC public health
|June 11, 2024
PubMed
概括

机器学习模型精确地选登革热病例,使用关键症状,如发烧和皮疹. 一个基于树的模型实现了98%的准确性,为医疗保健专业人员的智能手机应用程序铺平了道路.

关键词:
阿尔博病毒是阿尔博病毒.人工智能的人工智能是人工智能.临床症状 临床症状 临床症状医疗保健系统 医疗保健系统

更多相关视频

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.3K
A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

6.6K

相关实验视频

Last Updated: Jun 24, 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

1.4K
Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
06:00

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

Published on: January 26, 2024

1.3K
A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease
04:23

A Murine Model of Dengue Virus-induced Acute Viral Encephalitis-like Disease

Published on: April 28, 2019

6.6K

科学领域:

  • 公共卫生 公共卫生
  • 人工智能的人工智能
  • 流行病学 流行病学

背景情况:

  • 登革热感染导致全球显著的死亡率和发病率,需要改进控制和诊断策略.
  • 人工智能 (AI),特别是机器学习 (ML),为加强登革热管理提供了潜力.
  • 对登革热病例的早期和准确的查对于有效的公共卫生干预至关重要.

研究的目的:

  • 使用ML模型识别查登革热病例的关键变量.
  • 评估不同ML模型在分类登革热病例中的准确性.
  • 探索开发登革热查移动应用程序的可行性.

主要方法:

  • 利用了来自国家报告疾病监测系统 (SINAN) 的里约热内卢和米纳斯吉拉斯 (2016年,2019年) 报告的登革热病例数据.
  • 采用相互信息技术来确定与确诊的登革热病例相关的最相关的变量.
  • 训练并测试了ML模型 (物流回归,决策树,MLP) 在10,000个确诊和10,000个丢弃的登革热病例的数据集上,分为70%的训练和30%的测试集.

主要成果:

  • 确定了登革热查的十个关键变量:性别,年龄,发烧,肌痛,头痛,吐,恶心,背痛,皮疹和逆轨道疼痛.
  • 物流回归,决策树和多层感知器 (MLP) 模型表现出高性能.
  • 在使用开发的ML模型对登革热病例进行分类时,获得了最大98%的准确性.

结论:

  • 机器学习模型,特别是基于树的方法,对于精确的登革热病例查非常有效.
  • 鉴定的变量和高精度模型支持为医疗保健专业人员开发实用工具.
  • 使用基于树的模型的智能手机应用程序可以显著帮助快速识别和管理登革热.