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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

126
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
126
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

126
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...
126
Hazard Rate01:11

Hazard Rate

188
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
188
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.2K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.2K
Multimachine Stability01:25

Multimachine Stability

229
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
229

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

A New Estimation Algorithm for Destructive Cure Model: Illustration with Exponentially Weighted Poisson Competing Risks.

Communications in statistics: Simulation and computation·2026
Same author

A Support vector machine-based mixture cure model for mixed case interval censored data.

Statistics and computing·2026
Same author

A PINN-driven game-theoretic framework in limited data photoacoustic tomography.

Inverse problems·2025
Same author

Machine Learning Approach for Analyzing Mixed Case Interval Censored Data with a Cured Subgroup.

Advances in statistical analysis : AStA : a journal of the German Statistical Society·2025
Same author

Likelihood-Based Inference for Semi-Parametric Transformation Cure Models with Interval Censored Data.

Communications in statistics: Simulation and computation·2025
Same author

A New Cure Rate Model with Discrete and Multiple Exposures.

Communications in statistics: Simulation and computation·2025
Same journal

Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.

Statistics and computing·2026
Same journal

Subgroup Analysis of Differential Networks with Latent Variables.

Statistics and computing·2026
Same journal

Non-negative matrix factorization algorithms generally improve topic model fits.

Statistics and computing·2026
Same journal

Approximating evidence via bounded harmonic means.

Statistics and computing·2026
Same journal

Efficient Inference in First Passage Time Models.

Statistics and computing·2026
Same journal

Optimal <i>F</i>-score Matching for Bipartite Record Linkage.

Statistics and computing·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K

神経ネットワーク統合加速障害時間ベースの混合治癒モデル

Wisdom Aselisewine1, Suvra Pal1,2

  • 1Department of Mathematics, University of Texas at Arlington, Arlington, TX 76019, United States.

Statistics and computing
|August 25, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,治癒確率のニューラルネットワークを用いた新しい混合治癒率モデル (MCM) を導入し,生存分析における従来の方法を上回り,がん患者の予測精度を向上させます.

キーワード:
骨髄移植EMアルゴリズム長期生存者機械学習マルチプルアピュテーション

さらに関連する動画

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)
06:41

Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)

Published on: January 10, 2025

663

関連する実験動画

Last Updated: Sep 10, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)
06:41

Quantitative Analysis of Mitochondria-Associated Endoplasmic Reticulum Membrane (MAM) Stabilization in a Neural Model of Alzheimer's Disease (AD)

Published on: January 10, 2025

663

科学分野:

  • バイオ統計学
  • 機械学習
  • 生存分析

背景:

  • 混合治癒率モデル (MCM) は治癒したサブグループでの生存データで標準です.
  • ロジットリンクを備えた一般化された線形モデルを用いた伝統的な治癒確率モデリングは,複雑な共変量効果を捉えるのに限界がある.

研究 の 目的:

  • 治癒の可能性のためのニューラルネットワーク分類器を組み込んだ新しいMCMを導入する.
  • 治癒確率の推定と生存分析の予測精度を高めること.

主な方法:

  • 治癒確率のニューラルネットワークベースの分類器を搭載した新しいMCMを開発しました.
  • 治らない患者の生存分布に 加速された障害時間構造を用いた.
  • パラメータの推定に期待最大化アルゴリズムを使用した.

主要な成果:

  • 提案されたニューラルネットワークベースのMCMは,非線形分類境界を捕捉する上で優れた性能を示した.
  • ロジットベースのMCM,スプラインベースのMCM,その他の機械学習アルゴリズムをシミュレーションで上回った.
  • 精度と精度が向上し,予測の精度が向上しました.

結論:

  • 新しいMCMはニューラルネットワークを使って 複雑な治癒確率を効果的にモデル化します
  • 提案された方法は,生存データ分析の既存のアプローチよりも大幅に改善されています.
  • 白血病がん患者の生存データへの適用を通じて実用的な有用性を示した.