Jove
Visualize
联系我们

相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K
Survival Tree01:19

Survival Tree

73
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...
73

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

Food Insecurity and Binge Eating: Exploring Reward-Based Eating, Psychological Distress, and Diet Quality as Underlying Mechanisms.

Nutrients·2026
Same author

Brain reward function in young people with cannabis use disorder: A functional magnetic resonance imaging study.

Addiction (Abingdon, England)·2026
Same author

Gamified Assessment of Cognitive Impulsivity in Eating Disorders and Mental Ill-Health: Mixed Methods Study Incorporating Lived Experience Co-Design and Evaluation.

JMIR serious games·2026
Same author

Post-trial 12-month follow-up of TRACE (targeted research on addictive and compulsive eating) randomised controlled trial participants: A brief report.

Physiology & behavior·2026
Same author

From symptoms to spectra: testing the hierarchical taxonomy of psychopathology structure and its relation to functional outcomes.

Journal of mental health (Abingdon, England)·2026
Same author

How does cognition change over time in young people who use cannabis recreationally? A narrative review of the longitudinal literature to date.

Psychopharmacology·2026
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

相关实验视频

Updated: Jun 14, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K

在酒精恢复计划中分析学:一种机器学习方法.

Adele Collin1, Adrián Ayuso-Muñoz2, Paloma Tejera-Nevado2

  • 1CentraleSupélec, Université Paris-Saclay, 91190 Gif-sur-Yvette, France.

Journal of clinical medicine
|August 29, 2024
PubMed
概括

机器学习模型准确地预测酒精使用障碍治疗中断. 以前的物质使用和精神问题是关键预测因素,建议针对高危患者进行有针对性的干预.

关键词:
酒精使用障碍 饮酒障碍放弃 放弃 放弃 放弃机器学习是机器学习.结果就是结果.门诊患者 门诊患者现实世界的数据数据.治疗治疗治疗治疗治疗治疗

更多相关视频

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
05:12

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

883
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

9.7K

相关实验视频

Last Updated: Jun 14, 2025

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.1K
Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
05:12

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder

Published on: June 23, 2023

883
A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
08:05

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

9.7K

科学领域:

  • 数据科学数据科学数据科学
  • 机器学习 机器学习
  • 公共卫生 公共卫生

背景情况:

  • 酒精使用障碍 (AUD) 影响全球超过1亿人,需要有效的治疗保留.
  • 以往使用古典方法预测AUD治疗中断的研究得出了不确定的结果.
  • 需要新的机器学习方法来提高预测准确性,并为保留策略提供信息.

研究的目的:

  • 开发和验证用于预测AUD患者过早停止治疗的机器学习模型.
  • 通过使用先进的算法,高精度地识别掉学的主要预测因素.
  • 改善对那些处于更高中止治疗风险的个体的保留策略.

主要方法:

  • 对39030名AUD门诊患者 (2015-2019年) 的回顾性观察研究.
  • 应用各种机器学习算法,包括支持矢量分类器 (SVC),来预测治疗中断.
  • 利用可解释性技术来解释"黑子"模型并确定重要的预测因素.

主要成果:

  • 机器学习模型,特别是SVC,在预测AUD治疗中断方面表现出高精度.
  • 以前的药物使用和精神病并发性疾病成为放弃学业的重要预测因素.
  • 具体因素,如先前的阿片类药物替代治疗和协调的精神病治疗强烈表明了学风险.

结论:

  • 新型机器学习技术在大量AUD患者样本中有效预测治疗中断的更高风险.
  • 之前的物质使用障碍治疗和并发的精神疾病是主要的断学预测因素.
  • 具有这些特征的患者可能需要加强或补充干预措施,以获得成功的治疗参与.