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首页> 外文期刊>Journal of evaluation in clinical practice >Towards a new classification of stable phase schizophrenia into major and simple neuro‐cognitive psychosis: Results of unsupervised machine learning analysis
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Towards a new classification of stable phase schizophrenia into major and simple neuro‐cognitive psychosis: Results of unsupervised machine learning analysis

机译:朝着稳定期精神分裂症的新分类,成为主要和简单的神经认知精神病症:无监督机器学习分析的结果

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Abstract Rationale Deficit schizophrenia, as defined by the Schedule for Deficit Syndrome, may represent a distinct diagnostic class defined by neurocognitive impairments coupled with changes in IgA/IgM responses to tryptophan catabolites (TRYCATs). Adequate classifications should be based on supervised and unsupervised learning rather than on consensus criteria. Methods This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia. Results We found that using negative symptoms as discriminatory variables, schizophrenia patients may be divided into two distinct classes modelled by (A) impairments in IgA/IgM responses to noxious and generally more protective tryptophan catabolites, (B) impairments in episodic and semantic memory, paired associative learning and false memory creation, and (C) psychotic, excitation, hostility, mannerism, negative, and affective symptoms. The first cluster shows increased negative, psychotic, excitation, hostility, mannerism, depression and anxiety symptoms, and more neuroimmune and cognitive disorders and is therefore called “major neurocognitive psychosis” (MNP). The second cluster, called “simple neurocognitive psychosis” (SNP) is discriminated from normal controls by the same features although the impairments are less well developed than in MNP. The latter is additionally externally validated by lowered quality of life, body mass (reflecting a leptosome body type), and education (reflecting lower cognitive reserve). Conclusions Previous distinctions including “type 1” (positive)/“type 2” (negative) and DSM‐IV‐TR (eg, paranoid) schizophrenia could not be validated using machine learning techniques. Previous names of the illness, including schizophrenia, are not very adequate because they do not describe the features of the illness, namely, interrelated neuroimmune, cognitive, and clinical features. Stable‐phase schizophrenia consists of 2 relevant qualitatively distinct categories or nosological entities with SNP being a less well‐developed phenotype, while MNP is the full blown phenotype or core illness. Major neurocognitive psychosis and SNP should be added to the DSM‐5 and incorporated into the Research Domain Criteria project.
机译:摘要基本缺陷精神分裂症,如缺陷综合征的时间表所定义,可以代表由神经过度认知障碍定义的不同诊断类,其与IgA / IgM反应对色氨酸分子蛋白(Trycats)的变化相结合。足够的分类应基于受监督和无监督的学习而非共识标准。方法本研究使用机器学习作为提供稳定相思患者的更准确分类的手段。结果发现,使用负面症状作为歧视性变量,精神分裂症患者可分为由IgA / IgM损伤模型建模的两个不同类别,对毒性和一般更具保护的色氨酸分解代谢物,(B)在情节和语义记忆中的障碍,配对联想学习和虚假记忆创作,(c)精神病,激发,敌意,举动,消极和情感症状。第一簇显示出增加的阴性,精神病,刺激,敌意,举动,抑郁和焦虑症状,以及更多神经影响和认知障碍,因此称为“主要神经认知精神病症”(MNP)。第二簇被称为“简单的神经认知精神病症”(SNP)与正常控制相同的特征,尽管损伤较少显得比MNP较少。通过降低寿命质量,体重(反射睑体类型)以及教育(反映较低的认知储备),后者另外验证了后者。结论使用机器学习技术无法验证包括“1型”(阳性)/“2型”(负)和DSM-IV-TR(例如,偏执)精神分裂症的以前的区别。以前的疾病(包括精神分裂症)的名称并不是很足够的,因为它们没有描述疾病的特征,即相互关联的神经疫苗,认知和临床特征。稳定相思的精神分裂症由2个相关的定性不同的类别或姿态实体组成,SNP是一种较少发达的表型,而MNP是完全吹入的表型或核心疾病。应将主要神经认知精神病和SNP添加到DSM-5中,并纳入研究域标准项目。

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