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

Modeling in Therapy01:26

Modeling in Therapy

65
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
65

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

Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study.

JMIR AI·2025
Same author

Feelings of Patients Admitted to the Emergency Department.

Healthcare (Basel, Switzerland)·2025
Same author

Pre-hospital triage of children at risk of oesophageal button battery impaction: the button battery impaction score.

Clinical toxicology (Philadelphia, Pa.)·2024
Same author

Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges.

Journal of medical Internet research·2023
Same author

Prescribed anti-glaucoma medication consumption and road traffic crash.

Pharmacoepidemiology and drug safety·2022
Same author

Poison control centres and alternative forms of communication: comparison of response rates between text message and telephone follow-up.

Clinical toxicology (Philadelphia, Pa.)·2022

相关实验视频

Updated: Jun 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K

深度学习转换器模型用于构建一个全面的实时创伤观测站:开发和验证研究.

Gabrielle Chenais1, Cédric Gil-Jardiné1,2, Hélène Touchais1

  • 1Unit 1219, Bordeaux Public Health Center, Institut National de la Santé et de la Recherche Médicale, Bordeaux, France.

JMIR AI
|June 14, 2024
PubMed
概括

自动化自然语言处理 (NLP) 模型,特别是变压器,在对公共卫生监测的非结构化临床记录进行分类方面表现出高效率. GPTanam变压器模型在从电子健康记录中识别创伤病例方面取得了最佳表现.

关键词:
深度学习是一种深度学习.紧急情况,紧急情况.自然语言处理自然语言处理.公共卫生公共卫生.变压器 变压器 变压器创伤的创伤创伤的创伤.

更多相关视频

Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging
08:36

Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging

Published on: April 11, 2025

229
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

相关实验视频

Last Updated: Jun 23, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.8K
Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging
08:36

Development of an Uncomplicated Mild Traumatic Brain Injury Model Modified by Weight-Drop Method and Evidenced by Magnetic Resonance Imaging

Published on: April 11, 2025

229
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

386

科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.
  • 公共卫生监督 公共卫生监督

背景情况:

  • 公共卫生监测需要及时收集数据.
  • 自然语言处理 (NLP) 的进步使得从电子健康记录 (EHR) 中自动提取信息成为可能.

研究的目的:

  • 评估法国国家创伤观察中心的可行性.
  • 为了比较非结构化临床笔记的多类分类NLP方法.

主要方法:

  • 利用了来自法国急诊部 (2012-2019) 的69,110份临床笔记.
  • 手动注释的笔记用于创伤分类 (32.5%的患病率).
  • 训练了4个变压器模型,并将它们与SVM的TF-IDF进行了比较.

主要成果:

  • 变压器模型的性能优于SVM的TF-IDF.
  • 在GPTanam变压器模型中,在法语语体上进行了预训练,并通过自我监督学习进行了微调,实现了最高的性能 (微F1得分为0.969).

结论:

  • 变压器模型对于临床叙述数据的多类分类是有效的.
  • 未来的工作应该涉及缩写扩展和多输出分类.