Jove
Visualize
联系我们
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

相关概念视频

Bipolar Disorder01:30

Bipolar Disorder

1.3K
Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
1.3K
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

822
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...
822
Borderline Personality Disorder01:25

Borderline Personality Disorder

815
Borderline Personality Disorder is a complex and multifaceted mental health condition characterized by pervasive instability in interpersonal relationships, self-image, emotions, and impulse control. This instability manifests in extreme emotional reactions, fear of abandonment, and self-destructive behaviors. The disorder significantly impacts daily functioning, often leading to distress in both personal and professional domains.
Genetic and Environmental Contributions
Borderline Personality...
815
Mania and Antimanic Drugs: Overview01:24

Mania and Antimanic Drugs: Overview

724
Mania, a psychological condition characterized by elevated mood, increased energy, and reduced sleep need, is part of the bipolar disorder cycle. The exact cause of mania isn't entirely known, but it is thought to be a combination of genetic, environmental, and neurological factors. Bipolar disorder involves alternating manic and depressive episodes. Mood stabilizers like lithium, antipsychotics, and anticonvulsants help manage these episodes. Lithium carbonate is particularly effective as...
724
Bulimia Nervosa01:30

Bulimia Nervosa

931
Bulimia nervosa is a complex and severe eating disorder characterized by a cyclical pattern of binge-and-purge eating pattern. It generally involves an episode of binge eating, followed by compensatory behaviors such as vomiting, excessive exercise, laxative use, or fasting, to prevent weight gain. Despite often maintaining a normal weight, individuals with bulimia are intensely preoccupied with their body image and harbor an overwhelming fear of gaining weight. This can contribute to the...
931
Personality Theory by Eysenck and Eysenck01:29

Personality Theory by Eysenck and Eysenck

1.6K
Hans and Sybil Eysenck developed a widely recognized theory of personality, which emphasizes the role of temperament and genetically based differences in shaping individual traits. Their theory posits that biological factors primarily determine personality and can be understood through two main dimensions: extroversion/introversion and neuroticism/stability.
In the extroversion/introversion dimension, highly extroverted people are sociable, outgoing, and easily connect with others. In contrast,...
1.6K

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

TDGF1 Mediates the Oncogenic Effects of the OLMALINC/miR-3614-5p ceRNA Axis in Colon Cancer Through Nodal/Smad2 and Glypican-1/MAPK-AKT Signaling.

Cells·2026
Same author

The Role of Defect Geometry in Localized Emission from Monolayer Tungsten Dichalcogenides.

ACS nano·2026
Same author

Technology-driven revolution in CO<sub>2</sub> fixation: From natural pathways to programmable Biosystems.

Biotechnology advances·2026
Same author

Enterococcus faecalis Extracellular Vesicles Deliver the Bacterial GTPase Obg to Hijack mTOR Signalling in Hepatocellular Carcinoma.

Journal of extracellular vesicles·2026
Same author

Corrigendum to "Machine Learning Models for detecting Suicidal ideation in Chinese in-Patients with major depressive disorder: A Single-center retrospective study" [J. Affect. Disord. 406 (2026) 121679].

Journal of affective disorders·2026
Same author

A Systematic Comparison of Multiple Models for Depth-Dependent Decay of Hydraulic Conductivity in Salt Lake Areas: A Case Study of Typical Boreholes in the Qaidam Basin.

Water environment research : a research publication of the Water Environment Federation·2026

相关实验视频

Updated: Mar 13, 2026

Developing a Rat Model for Bipolar Disorder
04:42

Developing a Rat Model for Bipolar Disorder

Published on: May 2, 2025

1.6K

整合甲状腺功能和心理测量资料,以终身自杀企图风险分层在双相情感障碍:一个多算法机器学习研究.

Boyu Zhang1,2,3,4, Min Pan1,2,3,4, Anzhen Wang1,2,3,4

  • 1Department of Psychiatry, Affiliated Psychological Hospital of Anhui Medical University, Hefei, China.

Frontiers in psychiatry
|March 12, 2026
PubMed
概括

机器学习模型有效地预测双相情感障碍患者的自杀企图. 关键预测因素包括自杀念头,绝望和甲状腺刺激激素水平,有助于早期干预.

关键词:
双相情感障碍是双相情感障碍的一种疾病.机器学习是机器学习.预测模型的预测模型.自杀企图 自杀企图 自杀企图甲状腺功能 甲状腺功能

更多相关视频

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
04:33

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression

Published on: April 26, 2024

1.6K

相关实验视频

Last Updated: Mar 13, 2026

Developing a Rat Model for Bipolar Disorder
04:42

Developing a Rat Model for Bipolar Disorder

Published on: May 2, 2025

1.6K
Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
04:33

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression

Published on: April 26, 2024

1.6K

科学领域:

  • 精神病学和心理健康 精神病学和心理健康
  • 计算生物学和生物信息学
  • 临床诊断 临床诊断 临床诊断

背景情况:

  • 双极性障碍是一种严重的精神疾病,具有反复的情绪发作和低诊断率.
  • 自杀企图是双相情感障碍患者的一个重大问题,需要改善风险分层.
  • 横截面关联被用来探索终身自杀企图的预测因素.

研究的目的:

  • 开发和验证机器学习模型,用于对双相情感障碍患者终身自杀企图的风险分层.
  • 确定与该人口中自杀企图相关的关键临床和生物标志物.
  • 为临床医生提供工具,以识别患有早期干预风险较高的患者.

主要方法:

  • 利用机器学习技术,包括随机森林,梯度增强和支持向量机器.
  • 采用LASSO逻辑回归来选择变量和SMOTE来处理类不平衡.
  • 进行过敏感性分析以减轻反向因果偏差和对euthyroid患者的子组分析.

主要成果:

  • 随机森林模型表现出卓越的性能,精度为0.938和AUC为0.962.
  • 鉴定到的最重要的预测因素是自杀念头,教育水平,绝望,智障症状严重程度和甲状腺刺激激素 (TSH).
  • 敏感性和子组分析证实了所识别的预测因素和模型性能的稳定性.

结论:

  • 一个强大的机器学习模型被开发用于自杀企图的风险分层在双相情感障碍.
  • 该模型可以帮助临床医生识别有风险的个体,以便及时干预.
  • 未来的前性验证是必要的,以确认临床效用和建立时间优先级.