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Related Concept Videos

Schizophrenia01:17

Schizophrenia

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Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
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Psychological and Sociocultural Causes of Schizophrenia01:29

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Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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The term "psychosis" refers to a spectrum of mental disorders characterized by abnormal thoughts, perceptions, and behaviors. It can manifest as mood disorders, dementia, delirium with psychotic features, substance-induced psychosis with psychotic features, brief psychotic disorder, delusional disorder, schizoaffective disorder, and schizophrenia. Among all these disorders, schizophrenia is the most common psychotic disorder, affecting 1% of the worldwide population. Psychotic...
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Positive Symptoms Schizophrenia: Hallucinations and Delusions01:26

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Schizophrenia is a complex psychiatric disorder characterized by a range of symptoms that significantly impact cognition, behavior, and emotional regulation. Among these, the positive symptoms stand out as they involve the addition or exaggeration of normal mental functions, deviating markedly from typical behavior and perception. Hallucinations and delusions are prominent positive symptoms, each profoundly affecting the individual's experience of reality.
Hallucinations
Hallucinations in...
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Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Schizophrenia Detection Using Machine Learning Approach from Social Media Content.

Yi Ji Bae1, Midan Shim1,2, Won Hee Lee1

  • 1Department of Software Convergence, Kyung Hee University, Yongin 17104, Korea.

Sensors (Basel, Switzerland)
|September 10, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning accurately detects schizophrenia from social media text by analyzing linguistic patterns. This approach identifies key language markers, aiding in early detection and understanding of the disorder.

Keywords:
Redditlinguistic inquiry and word countmachine learningnatural language processingschizophreniasocial mediatopic modeling

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Artificial intelligence in healthcare

Background:

  • Schizophrenia is a leading cause of global disability, often untreated due to diagnosis challenges and stigma.
  • Social media offers a platform for individuals with schizophrenia to share experiences and seek support.
  • Machine learning (ML) shows promise for identifying mental health conditions from online text.

Purpose of the Study:

  • To evaluate the efficacy of ML in detecting schizophrenia markers in social media text.
  • To identify specific linguistic features and semantic patterns associated with schizophrenia in online posts.

Main Methods:

  • Collected Reddit posts related to schizophrenia and control topics (e.g., fitness, parenting).
  • Extracted linguistic features and content topics from posts.
  • Applied supervised ML for classification and unsupervised clustering for semantic analysis.

Main Results:

  • Identified significant linguistic differences, including increased use of third-person plural pronouns and negative emotion words.
  • Detected symptom-related topics prevalent in schizophrenia-related posts.
  • Achieved 96% accuracy in distinguishing schizophrenia posts from control posts using ML.

Conclusions:

  • Machine learning effectively identifies linguistic characteristics of schizophrenia in social media text.
  • Coherent semantic word groups are crucial for detecting schizophrenia.
  • ML-based analysis of social media can aid in identifying individuals with schizophrenia or at risk.