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Related Concept Videos

Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

48
The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
48
Theoretical Approaches to Psychological Disorder01:29

Theoretical Approaches to Psychological Disorder

117
The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
Biological approach
The biological approach posits that internal, organic factors are the primary causes of such disorders. This perspective emphasizes brain structure and function, genetic predispositions, and neurotransmitter imbalances. For example, schizophrenia has been associated with both genetic...
117
Treatment Strategies for Psychological Disorders01:24

Treatment Strategies for Psychological Disorders

100
Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
Psychological therapies focus on modifying emotions, thoughts, and behaviors through talking, interpreting, listening, rewarding, challenging, and modeling. Clinical psychologists, counselors, and social workers commonly practice psychotherapy. Clinical...
100
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

55
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...
55
Human Genetics01:28

Human Genetics

540
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...
540
Stress and Mental Health01:30

Stress and Mental Health

83
Chronic stress profoundly affects mental health, significantly influencing mood, behavior, and overall quality of life. Research closely links chronic stress with mental health conditions such as depression, anxiety, and substance use disorders. Ongoing exposure to stress can lead to physiological and psychological changes, initiating a cycle of emotional distress and maladaptive coping mechanisms.
Individuals with depression often experience challenges in both their personal and professional...
83

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Physical Unclonable Function Based Privacy-Preserving Authentication Scheme for Autonomous Vehicles Using Hardware Acceleration.

Sensors (Basel, Switzerland)·2026
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Related Experiment Video

Updated: Jun 11, 2025

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Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review.

Ujunwa Madububambachu1, Augustine Ukpebor2, Urenna Ihezue3

  • 1School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, Mississippi, United States of America.

Clinical Practice and Epidemiology in Mental Health : CP & EMH
|October 2, 2024
PubMed
Summary

Machine learning, particularly deep learning models like Convolutional Neural Networks (CNN), shows promise in predicting college student mental health conditions. Further research needs larger datasets and longitudinal data for improved accuracy.

Keywords:
AlgorithmCNNDeep learningMachine learningMental healthPrediction

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Area of Science:

  • Computer Science
  • Psychiatry
  • Data Science

Background:

  • Mental health conditions pose a significant challenge for college students.
  • Accurate prediction of these conditions is crucial for timely intervention and support.

Purpose of the Study:

  • To investigate the efficacy of machine learning algorithms in predicting mental health conditions among college students.
  • To systematically review existing literature on deep learning techniques for mental health diagnosis in student populations.

Main Methods:

  • A systematic literature review was conducted from 2011 to 2024.
  • Searches included terms like "deep learning" and "mental health" across major scientific databases.
  • PRISMA guidelines were followed, resulting in the analysis of 30 relevant studies.

Main Results:

  • Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Networks, and Extreme Learning Machine (ELM) are prominent predictive models.
  • CNN demonstrated superior accuracy in diagnosing bipolar disorder.
  • Key challenges include data limitations, heterogeneity of conditions, and the need for longitudinal data.

Conclusions:

  • Deep learning models, especially CNN, hold significant potential for predicting student mental health.
  • Addressing data quantity, diversity, and temporal dynamics is essential for advancing predictive accuracy.