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Related Concept Videos

Treatment Strategies for Psychological Disorders01:24

Treatment Strategies for Psychological Disorders

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

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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...
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Psychosurgery01:30

Psychosurgery

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Psychosurgery, the surgical alteration or permanent removal of brain tissue to alleviate severe psychological conditions, stands as one of the most radical and controversial treatments in the history of mental health care. Its development and application have evolved significantly, marked by dramatic shifts in scientific understanding and ethical perspectives.
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Introduction to Psychological Disorders01:19

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Abnormal behavior, often referred to as mental illness, results from changes in brain function that influence thought patterns, behaviors, and social interactions. Psychologists and psychiatrists typically assess abnormal behavior using three primary criteria: deviance, maladaptation, and personal distress, particularly when these traits persist over long periods.
Deviant Behavior
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Theoretical Approaches to Psychological Disorder01:29

Theoretical Approaches to Psychological Disorder

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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...
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Psychosis: Goals of Pharmacotherapy01:26

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Antipsychotic drugs are a crucial treatment method for acute and chronic psychoses, bipolar illness, and behavioral disorders. The selection of these drugs depends on several factors, including the state of the disease, clinical judgment, possible drug interactions, and the patient's sensitivity to adverse effects. In immediate scenarios, such as delirium and dementia, short-term treatment with low doses of high-potency typical or atypical agents can effectively manage symptom exacerbation.
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Machine learning methods in psychiatry: a brief introduction.

Zhirou Zhou1, Tsung-Chin Wu2, Bokai Wang1

  • 1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.

General Psychiatry
|February 25, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) is revolutionizing mental health research. This paper explores supervised and unsupervised learning applications in psychiatry, showcasing recent advancements and future potential for mental well-being.

Keywords:
modelspsychiatrystatistical

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Area of Science:

  • Psychiatry
  • Computer Science
  • Data Science

Background:

  • Machine learning (ML) techniques are increasingly applied to mental health challenges.
  • Understanding ML's role in psychiatry is crucial for advancing research.

Purpose of the Study:

  • To discuss the two primary ML approaches: supervised and unsupervised learning.
  • To illustrate ML implementation in recent mental health studies.

Main Methods:

  • Review of supervised learning applications in psychiatry.
  • Review of unsupervised learning applications in psychiatry.
  • Case examples of ML in mental health research.

Main Results:

  • Supervised learning models demonstrate utility in predicting mental health outcomes.
  • Unsupervised learning reveals novel patterns in psychiatric data.
  • Diverse ML applications show promise in various mental health domains.

Conclusions:

  • ML offers powerful tools for psychiatric research and clinical applications.
  • Further exploration of ML techniques can enhance understanding and treatment of mental health conditions.