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

114
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:
114
What is Weather?01:07

What is Weather?

18.2K
Overview
18.2K

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

Iron biomarkers for prognosis, diagnosis, and therapeutic insight in colorectal cancer.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
Same author

Nasal eosinophilia as a noninvasive biomarker of type 2 inflammation and treatment response in children with asthma.

The Journal of asthma : official journal of the Association for the Care of Asthma·2026
Same author

Bile Acid Dysregulation in Parkinson's Disease: Longitudinal Changes and Altered Metabolic Interactions.

Biomolecules·2026
Same author

Dance and Dietary Intervention Improves Metabolic Health, Fitness, and Quality of Life With Modest Gut Microbiota Shifts in Breast Cancer Patients With Obesity: A Pilot RCT.

Cancer reports (Hoboken, N.J.)·2026
Same author

Hierarchical composite outcomes in acute ischaemic stroke with large infarct: a win ratio analysis of the TENSION trial.

European stroke journal·2026
Same author

Adult-onset ocular flutter-opsoclonus spectrum across diverse neurological disorders: a video-oculographic case series.

BMC neurology·2026

相关实验视频

Updated: Jun 13, 2025

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.1K

使用气象时间序列预测极高的缺血性中风发病率.

Lucia Babalova1, Marian Grendar2,3, Egon Kurca1

  • 1Clinic of Neurology, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Bratislava, Slovakia.

PloS one
|September 11, 2024
PubMed
概括

使用天气数据预测极高的中风发病率是具有挑战性的. 气象因素并没有显著提高缺血性中风的预测准确度,这表明这些事件的预测能力有限.

更多相关视频

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model
05:32

Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model

Published on: August 11, 2023

1.8K

相关实验视频

Last Updated: Jun 13, 2025

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.1K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K
Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model
05:32

Author Spotlight: Assessing Ischemic Stroke Damage Through Middle Cerebral Artery Occlusion Model

Published on: August 11, 2023

1.8K

科学领域:

  • 神经学 神经学
  • 流行病学 流行病学
  • 气象学 天气学
  • 数据科学数据科学数据科学

背景情况:

  • 关于天气条件与中风发病率之间的联系存在不一致的发现.
  • 基于气象因素对中风发生率的预测建模是罕见的.
  • 这项研究调查了斯洛伐克的极端缺血性中风发病率和气象因素.

研究的目的:

  • 探索极端缺血性中风发生率与气象因素之间的关系.
  • 构建和评估预测模型,用于预测极高中风数量的日子.

主要方法:

  • 在五年 (2015-2019) 中分析了52,036例缺血性中风病例.
  • 使用历史记录的第90百分位数识别了极端中风日.
  • 采用后勤回归,时间序列的随机森林,以及Croston的一天前预测的方法.

主要成果:

  • 极端中风数量与气象因素之间的交叉相关性是可以忽略不计的.
  • 逻辑回归和随机森林的预测准确度与克罗斯顿的方法相当.
  • 这三种预测方法对极端中风事件的预测准确性有限.

结论:

  • 预测异常高中风发病率的日子是具有挑战性的.
  • 纳入气象参数并没有显著提高预测准确度.
  • 可能需要进一步的研究来确定极端中风事件的可靠预测因素.