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

Randomized Experiments01:13

Randomized Experiments

7.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
Regression Toward the Mean01:52

Regression Toward the Mean

6.5K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.5K
Survival Tree01:19

Survival Tree

159
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
159
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

741
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
741
Random Sampling Method01:09

Random Sampling Method

12.3K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
12.3K

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

[Research advances on the application of artificial intelligence technology in the diagnosis and treatment of sepsis patients].

Zhonghua shao shang yu chuang mian xiu fu za zhi·2025
Same author

[Statistical methods for extremely unbalanced data in genome-wide association study (2)].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2025
Same author

[Statistical design and application of clinical trials with small sample sizes for rare diseases].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2024
Same author

[Statistical methods for extremely unbalanced data in genome-wide association study (1)].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2024
Same author

[The emulation of clinical trials with real-world data: development and application of target trial].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2024
Same author

[A comparative study of multiple parallel mediation analysis methods].

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi·2022

関連する実験動画

Updated: Sep 10, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

[個別化された処理規則を推定するための重み付けされたランダムな森林]

Z Y Zhao1, M Y Lu2, F Shao1

  • 1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi
|August 25, 2025
PubMed
まとめ

この研究は,パーソナライズされた医療のための重量化されたランダムフォレスト法を導入し,多カテゴリー治療の推奨を改善します. このアプローチは,臨床意思決定における 個別化された治療規則の正確性と強さを高めます.

さらに関連する動画

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

8.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

関連する実験動画

Last Updated: Sep 10, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
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

8.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.6K

科学分野:

  • バイオ統計学
  • 機械学習
  • パーソナライズ された 医療

背景:

  • パーソナライズされた医療は 個々の患者に最適の治療の推奨を必要とする.
  • 現在の方法は,複数のカテゴリーの処理シナリオで精度と堅牢さで苦労しています.

研究 の 目的:

  • 個別化された処理規則のための新しい加重ランダム森林法を提案する.
  • 多重治療の設定における治療勧告の正確性と堅実性を向上させる.

主な方法:

  • 重み付けの分類タスクとして処理決定を策定した.
  • ランダムな森林の非パラメトリックで柔軟な性質を利用した.
  • 治療結果の間の期待される損失の差異を組み込んだ.

主要な成果:

  • 推計されたランダム・フォレスト・メソッドは,改善された推奨パフォーマンスを示した.
  • パーソナライズされた治療戦略のための実際の高血圧データに成功裏に適用されました.

結論:

  • 提案された方法は,複雑な環境における個別化された治療ルールの新しいアプローチを提供します.
  • データ主導の臨床意思決定システムの開発の可能性を示しています.
  • パーソナライズド医療における ウェイトされたランダムフォレストの価値を強調しています