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Regression Toward the Mean

Regression Toward the Mean

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...

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Updated: Jan 17, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

基于大脑的预测建模中的挑战概述:朝着有意义的预测洞察力

Vera Komeyer¹, Nicolás Nieto², Simon B Eickhoff²

¹Institute of Neuroscience and Medicine, Brain and Behavior, Forschungszentrum Jülich, Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Department of Biology, Faculty of Mathematics and Natural Sciences, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Institute of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Düsseldorf, Germany.

Biological psychiatry

|September 14, 2025

概括

机器学习 (ML) 和人工智能为精准精神病学提供了对大脑行为关系的洞察. 解决过度拟合和偏差等挑战对于该领域的有效和可概括的ML模型至关重要.

关键词:

大脑与行为之间的关联混是一种混.对交叉验证进行验证.统一化统一化统一化机器学习机器学习模型的解释性模型的解释性

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

相关实验视频

Last Updated: Jan 17, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

科学领域:

神经科学是一个神经科学.
计算精神病学是一种计算精神病学.
机器学习机器学习

背景情况:

机器学习 (ML) 和人工智能 (AI) 显示出精确精神病学和理解大脑行为联系的前景.
然而,混合结果突出了影响模型有效性和发现的关键挑战.

研究的目的:

解决应用ML/AI到大脑行为研究的关键挑战.
提高精神病学预测模型的可靠性和通用性.

主要方法:

批判性地评估交叉验证的局限性,并强调独立验证.
应用因果推理原则来识别和减轻混偏见.
审查多站点数据集的协调策略.
探索后期模型解释技术.

主要成果:

交叉验证可能会膨胀性能估计,需要独立验证.
混变量可能会导致ML模型的偏差;缓解策略是必不可少的.
多站点数据中的特定站点效应需要协调,以减少变化.
模型解释性方法可以提高透明度,但需要谨慎应用.

结论:

整合严格的验证,混控制和可解释性至关重要.
确保ML模型产生可靠的,可概括的发现,避免虚假的关联.
在精神病学研究中推进ML/AI的有效应用.