Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

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

Enhanced Near-Field Focusing via Interior Cavity and Nanogap Coupling in Disk-Cavity-Trench Nanostructures.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Multi-Ion Zeolite-Based Antimicrobial LDPE Films for Active Food Packaging: Ion Combinations and Performance in Real Food Systems.

Journal of food science·2026

Same author

Design and evaluation of an automated pediatric acute lymphoblastic leukemia registry from clinical data warehouses.

BMC medical informatics and decision making·2026

Same author

I<sup>2</sup>MoE: Interpretable Multimodal Interaction-aware Mixture-of-Experts.

Proceedings of machine learning research·2026

Same author

Glucose-mediated Maillard crosslinking of pea protein scaffolds to enhance mechanical properties and myoblast proliferation.

Food chemistry·2026

Same author

Neural Correlates of Personality Traits in Adolescents Exhibiting Excessive Smartphone Use: A Resting-State FMRI Study.

Life (Basel, Switzerland)·2025

Search research articles

相关实验视频

Updated: Sep 12, 2025

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

使用Llama 3.1从病理学报告中提取零射击临床数据.

Sunghyeon Park^1,2, Wona Choi¹, InYoung Choi¹

¹Department of Medical Informatics, College of Medicine, The Catholic University of Korea.

Studies in health technology and informatics

|August 8, 2025

概括

拉玛3.1大型语言模型在使用零射击学习从病理学报告中提取临床数据时实现了98%的准确性. 这证明了它在自动化医疗信息提取和改善医疗数据管理方面的潜力.

科学领域:

人工智能在医学中的应用
医疗保健的自然语言处理.
临床数据提取

背景情况:

病理学报告包含关键的临床信息,但通常是非结构化的自由文本.
手动提取这些数据是耗时且容易出现错误的.
需要自动化方法来提高医疗数据分析的效率和准确性.

研究的目的:

为了评估Llama 3.1大型语言模型 (LLM) 的性能,该模型具有700亿个参数 (70b) 用于临床信息提取.
评估模型在自由文本病理学报告上的零射击学习能力.
确定从非结构化医学文本直接生成结构化数据的可行性.

主要方法:

使用Llama 3.1 (70b) 模型进行零射击信息提取.
将模型应用于自由文本病理学报告的数据集.
评估提取的临床重要信息的准确性.
在JSON格式中生成结构化输出.

主要成果:

拉玛3.1模型在提取临床重要信息方面实现了98%的准确率.
该模型成功地在没有事先注释的情况下以JSON格式生成结构化数据.
在零射击学习场景中表现出强大的性能.

关键词:

提取信息提取信息在法律上,LLMs.

更多相关视频

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

相关实验视频

Last Updated: Sep 12, 2025

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

Published on: June 3, 2022

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

DNA-barcode-based Multiplex Immunofluorescence Imaging to Analyze FFPE Specimens from Genetically Reprogrammed Murine Melanoma

Published on: June 6, 2025

结论:

拉玛3.1显示了从复杂的医疗文件中自动提取数据的巨大潜力.
该模型可以通过提供准确,结构化的信息来简化临床工作流程.
这项技术可以提高医疗保健信息检索和分析的精度.