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

Systematic Sampling Method01:17

Systematic Sampling Method

10.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
Systematic sampling is one of the simplest methods...
10.3K
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.2K
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
1.2K

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

Advancing the Oncology Data Paradigm: Multisite Expansion of Structured Cancer Data Capture at the Point of Care.

JCO oncology practice·2026
Same author

Structured reasoning failures compromise LLM interpretation of clinical oncology notes.

NPJ digital medicine·2026
Same author

Bone-Modifying Agents in Metastatic Castration-Resistant Prostate Cancer.

JAMA network open·2026
Same author

Reply to: Prostate-Specific Membrane Antigen Positron Emission Tomography and Radiometabolic Therapies in Metastatic Castration-Resistant Prostate Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Impact of Race, Socioeconomic Status, and Clinicopathologic Features on Clinical Outcomes in Triple-Negative Breast Cancer in the ECOG-ACRIN EA1131 Trial.

JCO oncology advances·2026
Same author

Dynamic-EASIX DRI Model: A Novel Tool for Predicting Outcomes After Allogeneic Hematopoietic Cell Transplantation.

Transplantation and cellular therapy·2026

相关实验视频

Updated: Apr 24, 2026

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

15.8K

协作大型语言模型用于在活系统性审查中自动提取数据.

Muhammad Ali Khan1, Umair Ayub1, Syed Arsalan Ahmed Naqvi1

  • 1Department of Medicine, Mayo Clinic, Phoenix, AZ, 85054, United States.

Journal of the American Medical Informatics Association : JAMIA
|January 21, 2025
PubMed
概括

使用大型语言模型 (LLM) 在模拟的两位审稿人的过程中自动提取数据,显示了活系统性审稿的前景. 对不一致的反应进行交叉批评可以显著提高准确性.

关键词:
数据提取数据提取.大型语言模型.这是一个元分析.自然语言处理自然语言处理.系统性审查 系统性审查

更多相关视频

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K
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

484

相关实验视频

Last Updated: Apr 24, 2026

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

15.8K
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K
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

484

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 系统审查方法论 系统审查方法论

背景情况:

  • 数据提取是进行生活系统审查 (LSRs) 的耗时瓶.
  • 自动化这一过程对于高效和最新的证据综合至关重要.

研究的目的:

  • 开发和评估使用大型语言模型 (LLM) 的可通用,自动化数据提取工作流.
  • 模仿现实世界的两位审核员流程,以提高LSR数据提取的准确性.

主要方法:

  • 利用来自已发表的LSR的22个出版物的数据集,专注于23个关键变量.
  • 采用GPT-4-turbo和Claude-3-Opus进行数据提取,模拟两个审核员的工作流程,对不一致的响应进行交叉批评.
  • 使用准确度指标与黄金标准对比评估绩效.

主要成果:

  • 在快速开发组中观察到高一致性 (96%) 和准确性 (0.99).
  • 在持有测试组中,87%的答案与0.94准确度一致.
  • 交叉批评解决了51%的不一致反应,将其准确度提高到0.76.

结论:

  • 一致的LLM响应通常是准确的,交叉批评有效地提高了不一致的提取的准确性.
  • 基于LLM的模拟双审核员工作流提供了一种有效的数据提取方法,使真正活跃的系统审核成为可能.