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

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.1K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.1K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.6K
3.6K
Discharge Summary Forms01:31

Discharge Summary Forms

1.2K
The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
1.2K
Modeling and Similitude01:12

Modeling and Similitude

617
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
617

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

A systematic review of in vivo brain insulin resistance biomarkers in humans.

Biomarkers in neuropsychiatry·2026
Same author

REAl world Dementia OUTcomes (READ-OUT) protocol: observational study.

BMJ open·2026
Same author

Associations of plasma biomarkers of Alzheimer's disease pathology with modifiable risk factors and cognitive and functional outcomes: evidence from cross-sectional prediction and latent path analyses in the Bio-Hermes-001 cohort.

Alzheimer's research & therapy·2026
Same author

Midlife dementia risk and later dementia-related fear and avoidance: evidence from a prospective cohort study.

Aging & mental health·2026
Same author

An analysis on the role of glucagon-like peptide 1 receptor agonists in cognitive and mental health disorders.

Nature. Mental health·2026
Same author

The relative contribution of modifiable and non-modifiable factors for determining cognition in mid-life individuals at risk for late-life Alzheimer's disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026

相关实验视频

Updated: Jan 17, 2026

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

1.0K

SynthMedic:使用大型语言模型来生成合成排放总结,纠正和验证.

Georgi Grazhdanski1, Vasil Vasilev2, Sylvia Vassileva1

  • 1Faculty of Mathematics and Informatics, Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., Sofia, 1164, Bulgaria.

Journal of biomedical informatics
|September 17, 2025
PubMed
概括

研究人员开发了一种方法,使用大型语言模型 (LLM) 创建合成临床出院摘要. 这种方法确保了数据隐私,并且可以在没有真实患者信息的情况下用于训练AI.

关键词:
人工智能的人工智能是人工智能.临床文字 临床文本人类评估 人类评价知识图是知识图.大型语言模型.综合数据 综合数据

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

相关实验视频

Last Updated: Jan 17, 2026

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

1.0K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

科学领域:

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

背景情况:

  • 合成临床文本为训练人工智能模型提供了一个保护隐私的替代方案,而不是真实患者数据.
  • 目前用于生成合成医疗数据的方法在确保事实准确性和临床相关性方面面临挑战.
  • 合成数据的使用可以提高透明度,减少偏见,降低医疗AI开发的成本.

研究的目的:

  • 开发和验证一种方法来生成,验证和纠正使用LLM的合成排放摘要.
  • 为训练机器学习模型创建高质量的,公开可用的合成排放摘要集体.
  • 确保合成出院总结的医学事实正确性和临床可信度.

主要方法:

  • 使用大型语言模型 (LLM) 来生成基于特定疾病和医疗参考 (默克手册) 的合成出院摘要.
  • 采用基于LLM和人类专家验证来评估生成的摘要的质量和准确性.
  • 实施基于知识图的方法进行自动校正,以确保医疗事实准确性.

主要成果:

  • 人类专家的评估证实了合成释放总结的可信性和事实准确性,当它们与医学参考相关时.
  • 从医疗专业人员获得了94.35%的系统可用性得分,并从LLM获得了93.65%的忠诚度得分.
  • 生成的数据库包括900个综合性释放总结,针对九种重大疾病.

结论:

  • 提出的方法有效地产生高质量的合成排放摘要,适合训练AI模型.
  • 公开可用的语料库和方法使研究社区能够在不使用敏感患者数据的情况下开发先进的医疗AI.
  • 这项工作促进了人工智能工具的开发,以支持医疗保健专业人员完成日常任务.