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

125
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:
125

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

A continuous-discrete model of cell contraction incorporating actin and intermediate filaments.

iScience·2026
Same author

Retinal microvascular alterations in sickle cell disease: a systematic review and meta-analysis of OCTA findings based on genotype and stages of retinopathy.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026
Same author

MicroRNA Signatures in Alzheimer's Disease and Normal-Tension Glaucoma: A Comparative Expression Analysis of miR-128 and miR-455-3p.

Current Alzheimer research·2026
Same author

Cranial neuropathy during severe acute respiratory syndrome coronavirus 2 infection: a case report.

Journal of medical case reports·2025
Same author

Estimate the severity of acute ischemic stroke by optic nerve sheath ultrasound.

The ultrasound journal·2025
Same author

Trans-orbital sonography of the optic nerve in multiple sclerosis.

PloS one·2025

相关实验视频

Updated: Jun 28, 2025

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

预测从临床孤立综合征转化为多发性硬化症:一种可解释的机器学习方法

Saeid Rasouli1, Mohammad Sedigh Dakkali2, Reza Azarbad3

  • 1School of Medicine, Five Senses Health Research Institute, Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.

Multiple sclerosis and related disorders
|April 20, 2024
PubMed
概括

这项研究开发了一种可解释的机器学习模型,以预测临床确定的多发性硬化症 (CDMS) 从临床隔离综合症 (CIS) 转换. 该模型准确识别高风险患者,有助于个性化治疗和预防残疾.

关键词:
临床隔离综合征 临床隔离综合征可以解释的可解释性.机器学习是机器学习.模型模型模型模型模型多发性硬化症是多发性硬化症.预测 预测 预测在XGBoost上使用.

更多相关视频

Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice
05:44

Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice

Published on: October 13, 2023

1.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 28, 2025

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: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice
05:44

Author Spotlight: Creating a Versatile Experimental Autoimmune Encephalomyelitis Model Relevant for Both Male and Female Mice

Published on: October 13, 2023

1.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 医疗信息学 医疗信息学

背景情况:

  • 预测从临床隔离综合征 (CIS) 转化为临床确定的多发性硬化症 (CDMS) 对于个性化治疗至关重要.
  • 早期预测可以及时干预以防止残疾.

研究的目的:

  • 开发一个可解释的机器学习 (ML) 模型来预测CIS到CDMS的转换.
  • 利用人口统计,临床和成像数据进行预测.
  • 提高多发性硬化症 (MS) 患者临床决策的透明度.

主要方法:

  • 在273名墨西哥混血CIS患者的数据集上使用极端梯度提升 (XGBoost).
  • 使用交叉验证来选择特征,并使用持久套件进行测试.
  • 应用了SHapley添加式解释 (SHAP) 来实现模型的可解释性.

主要成果:

  • 确定了九个重要的预测变量,包括年龄,症状和成像/CSF标记.
  • 实现了83.6%的交叉验证准确率和91.8%的AUC.
  • 测试组准确度达到了78.3%和AUC达到了85.8%.

结论:

  • 可解释的ML模型有效地分层了CDMS转换的风险.
  • 促进个性化治疗决策和MS护理中的残疾预防.
  • 提供数值风险估计,提高临床决策透明度.