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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
56
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

44
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
44
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

75
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
75
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

87
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
87
Prediction Intervals01:03

Prediction Intervals

2.2K
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. 
2.2K
Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

3.0K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
3.0K

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

Surgical Patterns and Survival Outcomes in Young Women With Early-Stage Lung Adenocarcinoma: A Population-Based Analysis.

ANZ journal of surgery·2026
Same author

Gastrointestinal Motility-Induced Interplay in Pancreas Proton Therapy: Motion Simulation and Dosimetric Impact.

International journal of radiation oncology, biology, physics·2026
Same author

A transgenic rotifer-based RNA interference approach for antiviral protection against <i>Covert mortality nodavirus</i> in shrimp aquaculture.

Frontiers in microbiology·2026
Same author

An integrative framework combining Mendelian randomization, single-cell profiling, and experimental validation identifies FTMT as a mitochondrial-immune regulator in non-small cell lung cancer.

Cancer cell international·2026
Same author

Intratumoral bicarbonate functions as an adjuvant to potentiate PD-1 blockade in hepatocellular carcinoma.

Oncogene·2026
Same author

Pediatric Liver Transplantation: A Single-Center Retrospective Study.

Pediatric transplantation·2026

相关实验视频

Updated: Jun 5, 2025

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

8.2K

联合学习用于增强剂量-体积参数预测,使用分散的数据.

Jiahan Zhang1, Yang Lei1, Junyi Xia1

  • 1Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

Medical physics
|December 6, 2024
PubMed
概括

联合学习 (FL) 通过训练使用分布式,私有数据的中央模型,使精确的放射瘤规划成为可能. 这种方法与没有共享数据的集中模型性能相匹配,克服了采用障碍.

科学领域:

  • 辐射瘤学 辐射瘤学
  • 机器学习 机器学习
  • 医疗数据 隐私 医疗数据 隐私

背景情况:

  • 在放射性瘤学中,基于知识的规划受到数据稀缺和医疗数据共享的挑战的限制.
  • 这些局限性阻碍了先进规划技术的广泛采用.

研究的目的:

  • 评估联合学习 (FL) 的可行性,以克服辐射瘤学的数据共享限制.
  • 开发一种保护隐私的方法,用于训练使用分布式数据集的集中模型.

主要方法:

  • 一个渐变增强模型使用273个前列腺癌计划预测了膀和直肠剂量-体积指标 (V30Gy,V35Gy,V40Gy).
  • 联邦平均算法从10个模拟诊所子集中汇总了模型重量.
  • 用不同数量的站点和不平衡的数据分布来测试模型的稳定性.

主要成果:

  • FL模型的平均绝对误差 (MAE) 为4.7%±2.9%,明显低于单个模型 (6.5%±4.9%) 和可与集中模型 (4.4%±2.8%) 相比.
  • FL模型在不同数量的子集 (5-30) 中显示出稳定性,并且在不平衡的数据集上表现良好.
  • 在膀和直肠指标方面,FL方法的表现优于单个模型的36.7%.

结论:

关键词:
联合学习的联合学习基于知识的规划是基于知识的.机器学习是机器学习.治疗计划 治疗计划

更多相关视频

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

502
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

相关实验视频

Last Updated: Jun 5, 2025

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

8.2K
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

502
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
  • 联合学习为放射性瘤学中的基于知识的规划提供了一个可行的解决方案,在不集中敏感患者数据的情况下提高了预测准确性.
  • 即使在本地站点数据稀缺的情况下,FL模型也保持了高性能,比单独训练的模型更强大.