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相关概念视频

Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

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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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Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders

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Schizophrenia is a neurodevelopmental disorder whose origins are rooted in complex genetic components. Despite our burgeoning understanding, the pathophysiology of this disorder remains incompletely deciphered.
Researchers have identified genetic factors that increase susceptibility to schizophrenia, underscoring the intricate interplay between genetics and environment in disease development. At the core of schizophrenia's pathophysiology is excessive dopaminergic neurotransmission within...
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Psychosis and Antipsychotic Drugs: Overview01:28

Psychosis and Antipsychotic Drugs: Overview

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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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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Biological Causes of Schizophrenia01:29

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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.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
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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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Updated: May 30, 2025

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使用机器学习预测转化为精神病:对Cannon的回应

Jason Smucny1, Tyrone D Cannon2,3, Carrie E Bearden4,5

  • 1Department of Psychiatry, University of California, Davis, Davis, CA, United States.

Frontiers in psychiatry
|January 30, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测高风险个体的精神病转化. 然而,在一个数据集 (NAPLS-3) 上训练的模型在另一个数据集 (NAPLS-2) 上测试时显示精度降低,限制了临床应用.

关键词:
纳普尔斯 (Naples) 是一个城市.临床高风险 (CHR) 临床高风险可以概括的概括性.在抽样评估之外.精神病风险症状的规模.精神分裂症是一种精神分裂症.

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科学领域:

  • 神经科学是一个神经科学.
  • 计算精神病学是一种计算精神病学.

背景情况:

  • 机器学习模型在预测临床高风险 (CHR) 个体的精神病转化时表现出高准确度 (高达90%),使用北美Prodrome纵向研究-3 (NAPLS-3) 数据集.
  • 在独立数据集上测试模型概括对于验证预测能力至关重要.

研究的目的:

  • 通过对独立的NAPLS-2数据集进行测试,评估在NAPLS-3数据集上训练的机器学习模型的概括性.
  • 评估相同的机器学习算法在研究数据的先前代上的性能.

主要方法:

  • 使用了标准的机器学习算法.
  • 在NAPLS-3数据集上训练模型,以预测精神病转化.
  • 然后在NAPLS-2数据集上对训练过的模型进行测试.

主要成果:

  • 在NAPLS-2和NAPLS-3参与者之间的特征中观察到显著差异.
  • 所有机器学习模型都表现得超出了机会,天真贝叶斯和随机森林显示了最好的结果.
  • 在NAPLS-2数据集上的模型性能没有复制仅使用NAPLS-3数据集所看到的高精度.

结论:

  • 在一个数据集上训练的机器学习模型可以概括为独立的数据集,但性能可能会降低.
  • 目前的模型性能不足以用于心理病预测的直接临床应用.
  • 数据集之间参与者特征的差异可能导致了性能限制.