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

Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

51
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...
51
Depression: Overview01:18

Depression: Overview

219
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
219
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

61
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
61
Long-term Depression01:03

Long-term Depression

2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
2.5K

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

Mortality prediction for ICU patients with mental disorders using large language models ensemble and unstructured medical notes.

PloS one·2025
Same author

Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach.

Sensors (Basel, Switzerland)·2023
Same author

Henry gas solubility optimization double machine learning classifier for neurosurgical patients.

PloS one·2023
Same author

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques.

Diagnostics (Basel, Switzerland)·2023
Same author

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.

Journal of healthcare engineering·2023
Same author

COVID-19 Detection Mechanism in Vehicles Using a Deep Extreme Machine Learning Approach.

Diagnostics (Basel, Switzerland)·2023

相关实验视频

Updated: Jun 7, 2025

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

2.2K

可解释的多层动态组合框架,优化用于抑郁症检测和严重性评估.

Dillan Imans1, Tamer Abuhmed1, Meshal Alharbi2

  • 1College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, Republic of Korea.

Diagnostics (Basel, Switzerland)
|November 9, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个可解释的AI框架,使用动态集合学习来准确检测老年人的抑郁症和严重程度评估. 该模型实现了高精度,突出了改善心理健康诊断的关键健康因素.

关键词:
分类器优化优化的优化.抑郁症检测 抑郁症检测一个动态的合奏组合.可以解释的人工智能AI机器学习是机器学习.

更多相关视频

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

7.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

相关实验视频

Last Updated: Jun 7, 2025

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

2.2K
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

7.0K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

科学领域:

  • 医疗保健中的人工智能
  • 机器学习用于心理健康
  • 老年精神病学是一门精神病学专业.

背景情况:

  • 抑郁症对老年人产生重大影响,需要早期发现和干预.
  • 现有的诊断方法可能缺乏准确性和解释性.
  • 这项研究解决了老年精神健康评估中先进工具的需求.

研究的目的:

  • 开发和评估可解释的多层动态组合框架,用于抑郁症检测和严重程度评估.
  • 提高诊断准确度,并提供有关老年人抑郁症健康因素的见解.
  • 通过可解释的模型,增强人工智能在心理健康中的临床应用性.

主要方法:

  • 利用了来自国家社会生活,健康和衰老项目 (NSHAP) 的数据.
  • 采用了两阶段的框架,结合了经典的ML,静态组合和动态组合选择 (DES).
  • 综合可解释AI (XAI) 技术用于模型解释性.

主要成果:

  • FIRE-KNOP DES算法在抑郁症检测方面达到88.33%的准确性,在严重性预测方面达到83.68%的准确性.
  • 在XAI分析中,发现了影响抑郁症评估的显著心理和非心理健康指标.
  • 该框架在对抑郁症及其严重程度进行分类方面表现出了很高的有效性.

结论:

  • 动态组合学习显示了对心理健康评估,特别是对抑郁症的重大潜力.
  • 开发的框架为心理健康中的实际临床应用提供了坚实的基础.
  • 可解释的人工智能增强了机器学习模型在诊断和评估抑郁症严重程度方面的实用性.