Jove
Visualize
联系我们

相关概念视频

Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

83
Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
83
Randomized Experiments01:13

Randomized Experiments

6.9K
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...
6.9K
Human Genetics01:28

Human Genetics

566
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
566
Antidepressant Drugs: MAOIs and Other Agents01:23

Antidepressant Drugs: MAOIs and Other Agents

228
Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...
228
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

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

Leveraging Large Language Models and Machine Learning for Success Analysis in Robust Cancer Crowdfunding Predictions: Quantitative Study.

JMIR AI·2025
Same author

Robust Cancer Crowdfunding Predictions: Leveraging Large Language Models and Machine Learning for Success Analysis.

JMIR AI·2025
Same author

Evaluation of large language models within GenAI in qualitative research.

Scientific reports·2025
Same author

Assessment of cognitive function in bipolar disorder with passive smartphone keystroke metadata: a BiAffect digital phenotyping study.

Frontiers in psychiatry·2025
Same author

Water source, latrine type, and rainfall are associated with detection of non-optimal and enteric bacteria in the vaginal microbiome: a prospective observational cohort study nested within a cluster randomized controlled trial.

BMC infectious diseases·2024
Same author

Menstrual cups to reduce bacterial vaginosis and STIs through reduced harmful sexual and menstrual practices among economically vulnerable women: protocol of a single arm trial in western Kenya.

BMC public health·2024
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 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

利用随机效应机器学习算法来识别对抑郁症的脆弱性

Runa Bhaumik1, Jonathan Stange2

  • 1Department of Psychiatry, University of Illinois, Chicago, USA.

Journal of depression & anxiety
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型通过分析沉思和负面生活事件等因素,有效地识别出患抑郁症高风险的个体. 这些方法为有针对性的干预提供了潜力,以减少抑郁症的脆弱性.

关键词:
抑郁症 抑郁症 抑郁症机器学习 机器学习心理健康 心理健康随机的森林随机的森林回归树是一个回归树.

更多相关视频

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.3K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

相关实验视频

Last Updated: Jun 29, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.3K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

科学领域:

  • 精神病学是一个精神病学.
  • 计算精神病学是一种计算精神病学.
  • 医疗保健中的机器学习

背景情况:

  • 准确预测抑郁症进展对于改善患者的治疗结果至关重要.
  • 关于整合各种抑郁风险因素来识别高风险个体的研究有限.

研究的目的:

  • 应用数据驱动的机器学习 (ML) 方法来识别主要的抑郁风险因素.
  • 将ML模型的实用性与预测抑郁症的传统统计方法进行比较.

主要方法:

  • 利用随机效应/预期最大化 (RE-EM) 树和混合效应随机森林 (MERF) 算法.
  • 在185名年轻成年人的数据上训练有素的ML模型测量抑郁症风险因素和症状.
  • 使用交叉验证将ML模型性能与线性混合模型 (LMM) 进行比较.

主要成果:

  • RE-EM树和MERF有效地建模了复杂的相互作用,并确定了患抑郁症风险的子组.
  • ML模型显示,对于同时出现和潜在出现的抑郁症状,其预测准确度与LMMs相当.
  • 通过ML识别的关键预测因素包括沉思,负面的生活事件,负面的认知风格和感知控制.

结论:

  • 随机效应的ML模型显示出在抑郁症研究中临床实用性的巨大潜力.
  • 这些模型可以用来开发有针对性的干预措施,以减少抑郁症的脆弱性.