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

Automated Echocardiographic Detection of Congenital Heart Disease Using Artificial Intelligence.

Circulation·2026
Same author

How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.

Research square·2026
Same author

Modeling study of the suppression mechanism of acoustic liners on the thermoacoustic limit cycle oscillation in a Rijke tube.

The Journal of the Acoustical Society of America·2026
Same author

Analyzing Information Disparities across Modalities in Mortality Prediction.

medRxiv : the preprint server for health sciences·2025
Same author

Toward digital twins in the intensive care unit: a medication management case study.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Enhanced value of chest computed tomography radiomics features in breast density classification.

Scientific reports·2025
Same journal

DataAtlas: automatic generation of data dictionaries using large language models.

JAMIA open·2026
Same journal

An examination of the availability and characteristics of social needs data in the electronic health records: a path to social data harmonization and standardization at Johns Hopkins medicine.

JAMIA open·2026
Same journal

Generative artificial intelligence implementation in REDCap.

JAMIA open·2026
Same journal

Improving readability of layperson abstracts and summaries in oncology using task-specific large language model powered tool: results from the BRIDGE-AI 7 study.

JAMIA open·2026
Same journal

Accuracy of administrative data in ascertaining health conditions: a systematic review.

JAMIA open·2026
Same journal

Building a consumer health informatics introductory course consensus curriculum: an eDelphi study.

JAMIA open·2026
查看所有相关文章

相关实验视频

Updated: Jun 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

527

可概括的临床笔记部分识别与大型语言模型.

Weipeng Zhou1, Timothy A Miller2,3

  • 1Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington-Seattle, Seattle, WA 98195, United States.

JAMIA open
|August 14, 2024
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 显示出临床笔记部分识别的前景,GPT-4实现了高精度. 使用特定示例进行微调进一步提高了性能,使LLM几乎为此任务做好了生产准备.

关键词:
聊天GPT 聊天GPT 的意思GPT4 GPT4是什么意思精细调整 精细调整大型语言模型.部分标识 部分标识

更多相关视频

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K
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

2.4K

相关实验视频

Last Updated: Jun 17, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

527
A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

15.9K
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

2.4K

科学领域:

  • 自然语言处理自然语言处理.
  • 临床信息学 临床信息学
  • 医疗保健中的人工智能

背景情况:

  • 临床笔记部分的识别对于信息检索和下游NLP任务至关重要.
  • 传统的监督方法在不同临床数据集的可转移性方面面临挑战.
  • 大型语言模型 (LLM) 为克服这些局限性提供了一个潜在的解决方案.

研究的目的:

  • 评估LLM在临床注释部分识别方面的有效性.
  • 为了比较各种LLM的性能,包括GPT-4,GPT-3.5和开源模型.
  • 调查微调数据集大小和特异性对LLM绩效的影响.

主要方法:

  • 使用自由文本部分定义,将框架部分识别作为一个问题答案任务.
  • 在没有事先培训的情况下,评估了多个现成的LLM.
  • 使用不同大小和特异性的数据集,微调精选的LLM.

主要成果:

  • GPT-4获得了最高的F1得分 (0.77),超过了其他车型.
  • 对于特定的切口类型,GPT-4显示出高精度 (F1>0.9为33%,F1>0.8为56%).
  • 微调模型显示,较大的一般数据集的回报率下降,但在特定的部分识别示例中得到改善.

结论:

  • LLM,特别是GPT-4,对可泛化临床笔记部分识别非常有希望,并且正在接近生产准备.
  • 开源LLM正在迅速改善,并接近领先的专有模型的性能.
  • 通过将部分识别示例纳入LLM微调数据集,可以实现进一步的改进.