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

14.0K
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...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K

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

Medical School Cohorts and Preparedness to Work With Individuals From Different Backgrounds: A Cross-Sectional Study.

Health science reports·2026
Same author

Caught in the Web of Words: Do LLMs Fall for Spin in Medical Literature?

Proceedings of machine learning research·2025
Same author

Learned free-energy functionals from pair-correlation matching for dynamical density functional theory.

Physical review. E·2025
Same author

Leveraging Generative AI for Clinical Evidence Synthesis Needs to Ensure Trustworthiness.

ArXiv·2025
Same author

Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs.

Proceedings of machine learning research·2025
Same author

Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models.

Proceedings of machine learning research·2025
Same journal

Visual Self-Refinement for Autoregressive Models.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
Same journal

README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
Same journal

MedCOD: Enhancing English-to-Spanish Medical Translation of Large Language Models Using Enriched Chain-of-Dictionary Framework.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
Same journal

Large Language Models are In-context Teachers for Knowledge Reasoning.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
Same journal

Using tournaments to calculate AUROC for zero-shot classification with LLMs.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
Same journal

Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing·2026
查看所有相关文章

相关实验视频

Updated: Jan 7, 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

983

奇尔:用大型语言模型从临床笔记中零射击定制可解释特征提取.

Denis Jered McInerney1, Geoffrey Young2, Jan-Willem van de Meent3

  • 1Northeastern University.

Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing
|December 31, 2025
PubMed
概括
此摘要是机器生成的。

CHiLL (Crafting High-Level Latents) 使用大型语言模型 (LLM) 来生成线性模型的健康记录中的可解释特征. 这种方法赋予了医生权力,并实现了与手动特征提取相美的性能.

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

1.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

相关实验视频

Last Updated: Jan 7, 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

983
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

1.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

科学领域:

  • 人工智能的人工智能
  • 临床信息学 临床信息学
  • 机器学习 机器学习

背景情况:

  • 电子健康记录 (EHR) 包含大量的数据.
  • 从EHR中提取临床上有意义的特征是具有挑战性的.
  • 医生的专业知识对于有效的风险预测至关重要.

研究的目的:

  • 引入CHiLL (Crafting High-Level Latents),这是一种用于线性模型特征的自然语言规范的新方法.
  • 为了使医生能够利用他们的领域知识从EHR数据中进行特征工程.
  • 改善临床预测建模中的解释性和性能.

主要方法:

  • CHiLL使用专家设计的查询提示大型语言模型 (LLM) 来生成来自健康记录的特征.
  • 生成的特征用于训练简单的线性分类器.
  • 该方法使用MIMIC-III和MIMIC-CXR数据集进行评估,用于30天再接收预测等任务.

主要成果:

  • 用CHiLL生成的特征训练的线性模型表现出与使用参考特征的模型可比的性能.
  • 与使用"袋词"特征的线性模型相比,CHiLL提供了更好的解释性.
  • 学习特征权重显示与临床预期有很强的一致性.

结论:

  • CHiLL提供了一种有效的方法,可以使用LLMs从EHR数据中生成可解释的特征.
  • 该方法通过将其专业知识整合到功能工程过程中,赋予临床医生权力.
  • CHiLL显示了增强临床风险预测模型的前景,并提高了可解释性和性能.