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
Prediction Intervals01:03

Prediction Intervals

3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.1K
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

237
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
237

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

Opioid-specific risk of respiratory depression in non-cancer pain: a retrospective cohort study.

BMC medicine·2026
Same author

Recognition and linking of discontinuous named entities in healthcare: a comparative performance analysis.

Frontiers in digital health·2026
Same author

Corticospinal tract risk modifies motor recovery after minimally invasive surgery for intracerebral hemorrhage: a secondary analysis of MISTIE-III.

medRxiv : the preprint server for health sciences·2026
Same author

Health inequalities in outpatient neurological conditions across a large UK urban population: a retrospective observational study using automated coding.

BMJ neurology open·2026
Same author

Development and validation of clinical prediction models for personalized renal function monitoring in people with heart failure in primary care: the RENAL-HF study protocol.

European heart journal. Digital health·2026
Same author

Transcriptomic profiling of secukinumab-treated psoriatic arthritis reveals potential novel response-associated pathways.

Rheumatology (Oxford, England)·2026

相关实验视频

Updated: Jan 12, 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

大型语言模型会改变临床预测吗?

Yusuf Yildiz1, Goran Nenadic2, Meghna Jani3

  • 1Faculty of Biology, Medicine and Health, School of Health Sciences, Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK. yusuf.yildiz@postgrad.manchester.ac.uk.

Diagnostic and prognostic research
|November 6, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 显示了使用电子健康记录改善临床预测模型的潜力. 然而,为了有效整合医疗保健,必须解决方法,验证,基础设施和监管方面的挑战.

关键词:
这是一个偏见的偏见.临床预测模型的临床预测模型.公平的 公平的 公平的大型语言模型.监管和报告工作 监管和报告工作

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
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 12, 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
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:

  • 人工智能在医学中的应用
  • 医疗信息学 医疗信息学
  • 临床决策支持 临床决策支持

背景情况:

  • 大型语言模型 (LLM) 在医疗保健应用中越来越受欢迎.
  • 临床预测模型 (CPM) 对于诊断和预后至关重要.
  • 电子健康记录 (EHR) 包含丰富的纵向患者数据.

研究的目的:

  • 评估LLM在增强CPM方面的潜力.
  • 评估LLM在处理临床预测的纵向EHR数据方面的能力.
  • 确定在医疗保健中LLM整合的挑战和机会.

主要方法:

  • 对LLMs在临床预测中的应用进行审查和评论.
  • 专注于LLM在处理多式联络和纵向EHR数据方面的能力.
  • 对CPM的LLM实施现有挑战的分析.

主要成果:

  • 在分析复杂的EHR数据以进行多结果预测方面,LLM表现有前途.
  • 仍然存在重大挑战,包括时间到事件建模,预测校准和外部验证.
  • 模型中的偏见和缺乏监管框架阻碍了广泛采用.

结论:

  • 跨学科的合作对于公平有效地将LLMs纳入临床预测是必不可少的.
  • 优先考虑时间意识,公平和可解释的模型是改变临床工作流程的关键.
  • 解决方法和监管障碍对于实现医疗保健中的LLM潜力至关重要.