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

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

Frailty Status and Outcomes in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention: a multicentre retrospective cohort study.

European journal of cardiovascular nursing·2026
Same author

GLP-1 Receptor Agonists for Weight Loss and Risk of Major Safety Outcomes: A Multicentre Cohort Study.

Diabetes, obesity & metabolism·2026
Same author

Impact of race-neutral GLI reference equations in Northeast Asian patients with IPF.

BMJ open respiratory research·2026
Same author

Advancing Gastric Cancer Chemoprevention: Early Signals and Ongoing Challenges.

Cancer prevention research (Philadelphia, Pa.)·2026
Same author

Glucagon-Like Peptide-1 Receptor Agonists and Risk of Mental Health Disorders in Type 2 Diabetes: Active Comparator, New User Cohort Study.

Diabetes, obesity & metabolism·2026
Same author

Impact of indoor exposure to particulate matter on the progression of idiopathic pulmonary fibrosis: a prospective multicentre cohort study protocol.

BMJ open respiratory research·2026
Same journal

A GenAI Pipeline for Violinist Kinematic Data Management.

Studies in health technology and informatics·2026
Same journal

AMAL-For-Qatar: A Comprehensive AI Ecosystem for Fetal Ultrasound Analysis - Project Overview and Achievements.

Studies in health technology and informatics·2026
Same journal

Longitudinal Treatment-Aware Multimodal AI for Dermatology: A Scoping Review.

Studies in health technology and informatics·2026
Same journal

Predicting Postpartum Depression Using Imbalance-Aware Machine Learning.

Studies in health technology and informatics·2026
Same journal

Validation of Deep-Learning Models for Autosegmentation of Brain Metastases.

Studies in health technology and informatics·2026
Same journal

Delay-Dependent Gating in Modular RNNs.

Studies in health technology and informatics·2026
查看所有相关文章

相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K

韩国临床笔记的非识别模型:使用深度学习模型

Junhyuk Chang1, Jimyung Park1, Chungsoo Kim1

  • 1Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Korea.

Studies in health technology and informatics
|January 25, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种使用微调的BERT深度学习方法去识别模型,以保护临床记录中的患者健康信息 (PHI). 该模型获得了高的F1分数,证明了它在安全数据提取方面的有效性.

关键词:
电子健康记录电子健康记录自然语言处理自然语言处理.

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
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

1.5K

相关实验视频

Last Updated: Jul 5, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.0K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K
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

1.5K

科学领域:

  • 临床信息学 临床信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 从临床记录中提取信息需要去识别,以保护患者健康信息 (PHI).
  • 现有的方法可能不足以在自由文本临床笔记中准确地删除PHI.

研究的目的:

  • 开发和评估一个深度学习模型,用于消除临床记录的识别.
  • 创建一个全面的受保护健康信息 (PHI) 实体清单,用于模式培训.

主要方法:

  • 微调BERT深度学习模型.
  • 使用一个精心策划的受保护健康信息 (PHI) 实体列表.
  • 实施一个强大的非识别预处理管道.

主要成果:

  • 微调的BERT模型获得了0.924.9的严格F1得分.
  • 该模型在识别和从临床文本中删除PHI方面表现出很高的准确性.

结论:

  • 开发的基于BERT的非识别模型是有效的,适合临床数据.
  • 这种方法有助于从敏感的患者记录中安全地提取信息.