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

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:

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

Integrating physical fitness and psychology: an in-depth discussion of exercise interventions for weight reduction among university students.

Frontiers in psychology·2026
Same author

Process-dependent niches of rpf-harboring microorganisms regulate nitrogen and carbon functional networks in full-scale activated sludge.

Environmental research·2026
Same author

Debt as a blessing: A capital screening mechanism.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Bifunctional Photochemical Performance Based on Pt-TiO<sub>2</sub> Hollow Sphere Schottky Junction: From Photocatalytic Hydrogen Production to Highly Sensitive Glucose Sensing.

Inorganic chemistry·2026
Same author

Amphiphilic Bonding Intercalation Reshapes Active Sites and Interlayer Microenvironment for Selective and Stable Seawater Oxidation.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

PAIRMAP: A Unified Geometry-Aware Pairwise-Map Framework for Molecular Representation Learning.

Journal of chemical information and modeling·2026

相关实验视频

Updated: Jul 11, 2026

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-&kappa;B Reporter Transgenic
08:51

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

Published on: September 4, 2012

19.2K

基于机器学习的非puerperal乳腺炎患者的复发模型.

Gaosha Li1,2, Qian Yu1, Feng Dong3

  • 1Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.

PloS one
|January 17, 2025
PubMed
概括

一个新的机器学习模型准确地预测了非乳腺性乳腺炎 (NPM) 的复发. 这种工具有助于个性化治疗,改善治疗结果,降低患有这种炎症性乳腺疾病的患者复发风险.

更多相关视频

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

6.7K

相关实验视频

Last Updated: Jul 11, 2026

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-&kappa;B Reporter Transgenic
08:51

Intraductal Injection of LPS as a Mouse Model of Mastitis: Signaling Visualized via an NF-κB Reporter Transgenic

Published on: September 4, 2012

19.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
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

6.7K

科学领域:

  • 在瘤学瘤学.
  • 传染性疾病 传染性疾病
  • 免疫学 免疫学 免疫学

背景情况:

  • 非乳腺性乳腺炎 (NPM) 是一种炎症性乳腺疾病,影响哺乳期以外的女性.
  • NPM具有很高的复发倾向,需要有效的预测策略.
  • 目前的诊断方法缺乏NPM复发的预测模型.

研究的目的:

  • 开发和验证一种机器学习模型,用于预测非乳腺乳腺炎复发.
  • 通过数据分析,确定NPM复发的关键预测因素.

主要方法:

  • 从120名NPM患者的实验室数据的回顾性分析.
  • 使用后勤回归,XGBoost,随机森林和AdaBoost算法开发一个预测模型.
  • 使用内部测试和外部验证队伍验证模型.

主要成果:

  • 选择了后勤回归模型作为最佳预测因素,利用FIB,细菌感染和CD4+T细胞计数.
  • 该模型在训练组中达到0.846的AUC,在测试组中达到0.833.
  • 外部验证显示了强的性能,AUC为0.825.

结论:

  • 开发的机器学习模型有效地预测了NPM的复发.
  • 该工具支持NPM患者个性化辅助治疗决策.
  • 该模型有助于提高治疗效率并最大限度地降低复发率.