Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

40
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
40
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

298
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...
298
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

2.3K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
2.3K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

424
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
424
Combined Effects of Drugs: Synergism01:27

Combined Effects of Drugs: Synergism

7.0K
Synergism is a useful mechanism where combining two or more drugs is more effective than each constituent used alone. Such combinations are also called supra-additive interactions. The drugs collectively enhance the final therapeutic effect by acting on different targets. Another advantage is that the low dose of each constituent drug is sufficient to achieve the desired effect. This helps reduce the duration of therapy and lower the adverse effects of these drugs.
Such synergistic combinations...
7.0K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

649
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...
649

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Bright nanoparticle-labeled lateral flow immunoassay for rapid quantitative detection of tacrolimus in human plasma.

Mikrochimica acta·2025
Same author

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model.

Journal of visualized experiments : JoVE·2025
Same author

An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury.

EClinicalMedicine·2025
Same author

Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.

Frontiers in immunology·2025
Same author

Serum Albumin Levels and Length of Stay in Premature Infants.

Indian journal of pediatrics·2025
Same author

A novel role of the antidepressant paroxetine in inhibiting neuronal Kv7/M channels to enhance neuronal excitability.

Translational psychiatry·2025

関連する実験動画

Updated: Feb 26, 2026

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

1.7K

CL-MHAD：処方推薦のための対照学習ベースのマルチハイパーグラフ集約および拡散モデル

Juanzi Zhou1, Yin Zhang2, Fang Hu1

  • 1College of Information Engineering, Hubei University of Chinese Medicine, Wuhan, 430065, China.

Artificial intelligence in medicine
|February 24, 2026
PubMed
まとめ
この要約は機械生成です。

本研究では、漢方薬（TCM）処方推薦のための新しいモデルであるCL-MHADを紹介します。これは、パーソナライズされた診断と治療のために多次元のハーブ知識を効果的に融合することにより、精度を向上させます。

キーワード:
クロスビュー対照学習データ拡張特徴融合ハイパーグラフ集約と拡散損失関数再構築処方推薦

さらに関連する動画

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

1.2K

関連する実験動画

Last Updated: Feb 26, 2026

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

1.7K
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

1.2K

科学分野:

  • 人工知能
  • 計算生物学
  • 伝統医学（TCM）

背景:

  • TCMにおけるパーソナライズされた診断と治療は、症候群ベースの処方推薦に依存しています。
  • 正確なTCM推薦のための多次元ハーブ知識の抽出と融合は困難です。

研究 の 目的:

  • CL-MHAD、すなわち対照学習ベースのマルチハイパーグラフ集約および拡散モデルを提案し、TCM処方推薦を改善すること。
  • 多次元のハーブ知識の効果的な抽出と融合という課題に対処すること。

主な方法:

  • 処方、ハーブ、特性、および投与量に焦点を当てたマルチビューハイパーグラフ再構築メカニズムを開発しました。
  • 高次の関係を捉えるための拡散強化法と、クロスビュー対照学習戦略を実装しました。
  • データスパース性を軽減するためのトポロジー認識ランダムウォーク拡張と、最適化のための統合損失関数を利用しました。

主要な成果:

  • CL-MHADは、胃腸疾患（GID）の実際の臨床設定において、ベースラインモデルよりも優れたパフォーマンスを示しました。
  • 複数の評価指標で1.27％から24.67％のパフォーマンス向上の達成。
  • 重み付け融合戦略の有効性と、データスパース性に対するモデルの堅牢性を検証しました。

結論:

  • CL-MHADは、TCMにおける正確な症候群ベースの処方推薦のための効果的なソリューションを提供します。
  • 提案されたモデルは、TCMにおける個別化医療を進歩させるための有望なパラダイムを提示します。