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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
34
Improving Translational Accuracy02:07

Improving Translational Accuracy

2.5K
2.5K

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

Are machine learning models superior to logistic regression models to predict 30-day mortality post-hip fracture surgery?

JBMR plus·2026
Same author

Benefits and harms of antiresorptive therapy in men with non-metastatic prostate cancer on androgen deprivation therapy: A systematic review and meta-analysis of randomized controlled trials.

Metabolism: clinical and experimental·2026
Same author

Intraoperative PTH monitoring: does sampling site or assay generation matter?

Journal of the Endocrine Society·2026
Same author

Sex-Biased Pharmacotherapeutic Disparities in Hypertension.

Drugs·2026
Same author

Development and validation of a rule-based tool for quality management reporting in a genetics laboratory.

Practical laboratory medicine·2026
Same author

Pregnancy and Lactation Associated Osteoporosis: A Systematic Review.

Calcified tissue international·2026

相关实验视频

Updated: May 15, 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

466

简化系统性审查,使用大型语言模型,使用快速工程和检索增强生成.

Fouad Trad1, Ryan Yammine2, Jana Charafeddine3

  • 1Department of Electrical and Computer Engineering, American University of Beirut, Beirut, Lebanon. fat10@mail.aub.edu.

BMC medical research methodology
|May 10, 2025
PubMed
概括

大型语言模型 (LLM) 通过自动化文献选,显著提高系统审查 (SR) 的效率. 基于LLM的系统,与手动方法和Rayyan相比,减少了95.5%的选时间,同时保持了较低的虚假阴性率 (FNR).

关键词:
大型语言模型.快递工程是指快递的工程.雷扬AI AI 雷扬AI 雷扬AI提取增强生成的提取.系统审查是系统的审查.

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

15.7K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.0K

相关实验视频

Last Updated: May 15, 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

466
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

15.7K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.0K

科学领域:

  • 医疗信息学 医疗信息学
  • 基于证据的医学基于证据的医学.
  • 研究中的人工智能.

背景情况:

  • 系统审查 (SRs) 对于基于证据的指南至关重要,但涉及时间密集的文献选.
  • 大型语言模型 (LLM) 具有加速SR过程的潜力.

研究的目的:

  • 为了比较商业工具 (Rayyan) 和基于LLM的内部系统用于自动化SR文献选的效率.
  • 通过手动选来评估两个自动化系统的性能指标.

主要方法:

  • 已完成的维生素D和落的SR (14,439篇文章) 用作比较.
  • 雷扬接受了2000篇文章的培训,并对其余的文章进行了分类.
  • 一个LLM系统利用快速工程进行标题/摘要选和检索增强生成 (RAG) 进行全文选.

主要成果:

  • 该LLM系统实现了99.5%的文章排除率 (AER) 和100%的负预测值 (NPV),将手动选时间减少了95.5% (总共25.5小时).
  • 雷扬,在"可能排除"的门下,实现了0%的FNR和50.7%的AER,但将查时间增加到81.3小时.
  • 该LLM系统成功识别了所有相关文章,同时显著减少了整体选工作.

结论:

  • 与手动选和商业工具 (如Rayyan) 相比,基于LLM的系统大大提高了SR的效率.
  • 该LLM方法保持低假负率,确保全面纳入相关研究.