Department of Psychiatry, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Research Affairs, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
J Eval Clin Pract. 2018 Aug;24(4):879-891. doi: 10.1111/jep.12945. Epub 2018 May 23.
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.
This study used machine learning as means to provide a more accurate classification of patients with stable phase schizophrenia.
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).
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.
根据缺陷综合征时间表定义的缺陷型精神分裂症,可能代表一个由神经认知障碍加上对色氨酸分解产物(TRYCATs)的 IgA/IgM 反应变化定义的独特诊断类别。适当的分类应该基于监督和无监督学习,而不是共识标准。
本研究使用机器学习作为手段,为稳定期精神分裂症患者提供更准确的分类。
我们发现,使用阴性症状作为判别变量,精神分裂症患者可以分为两个不同的类别,模型为(A)对有害和一般更具保护性的色氨酸分解产物的 IgA/IgM 反应受损,(B)情节和语义记忆、配对联想学习和错误记忆创建以及(C)精神病、兴奋、敌意、怪癖、阴性和情感症状受损。第一个聚类显示出更多的阴性、精神病、兴奋、敌意、怪癖、抑郁和焦虑症状,以及更多的神经免疫和认知障碍,因此称为“主要神经认知精神病”(MNP)。第二个聚类,称为“简单神经认知精神病”(SNP),与正常对照组有相同的特征,但损害程度不如 MNP 严重。后者通过降低生活质量、体重(反映瘦体型)和教育(反映认知储备较低)得到外部验证。
使用机器学习技术无法验证以前的区分,包括“1 型”(阳性)/“2 型”(阴性)和 DSM-IV-TR(例如偏执型)精神分裂症。以前的疾病名称,包括精神分裂症,都不是很合适,因为它们没有描述疾病的特征,即相互关联的神经免疫、认知和临床特征。稳定期精神分裂症由 2 种相关的、本质上不同的类别或分类实体组成,SNP 是一种不太发达的表型,而 MNP 是完全发展的表型或核心疾病。主要神经认知精神病和 SNP 应添加到 DSM-5 中,并纳入研究领域标准项目。