精神病学仍然处于永久的危机状态,将精神病学从医学领域中分离出来。精神病学的危机是由许多不同的竞争方法来证明的精神疾病,包括心理动力学,生物,分子,泛经济学,精度,认知和现象学精神病学,民间心理学,心脑二元论,描述性精神病理学,和后精神病学。当前的“黄金标准”精神疾病诊断和统计手册/情绪障碍和精神分裂症的国际疾病分类分类是不可靠的,并且无法采用演绎推理方法。因此,心境障碍和精神分裂症的研究无法修改常规分类,也没有提供更充分的治疗方法,这并不奇怪。本文的目的是解释新的标称网络精神病学(NNP)方法,它使用机器学习方法通过组合风险-抵御能力来构建数据驱动的精神疾病因果模型,不良结局途径(AOP),认知组,脑门,分期,症状,以及因果模型中惊人的潜在分数。后者可以接受训练,用偏最小二乘分析进行了测试和验证。这种方法不仅允许计算通路表型或生物特征,还要构建可靠和可复制的模拟网络,它们是,因此,可推广为疾病模型。在将经过验证的特征向量集成到拟合良好的标称网络中之后,聚类分析可以应用于R/R的潜在变量得分,AOP,认知组,脑门,和表型潜伏载体。这种模式识别方法可能会暴露新的(诊断)患者类别,如果在独立样本中进行交叉验证,则可能构成新的(诊断)疾病类别。
Psychiatry remains in a permanent state of crisis, which fragmented psychiatry from the field of medicine. The crisis in psychiatry is evidenced by the many different competing approaches to psychiatric illness including psychodynamic, biological, molecular, pan-omics, precision, cognitive and phenomenological psychiatry, folk psychology, mind-brain dualism, descriptive psychopathology, and postpsychiatry. The current \"gold standard\" Diagnostic and Statistical Manual of Mental Disorders/International Classification of Diseases taxonomies of mood disorders and schizophrenia are unreliable and preclude to employ a deductive reasoning approach. Therefore, it is not surprising that mood disorders and schizophrenia research was unable to revise the conventional classifications and did not provide more adequate therapeutic approaches. The aim of this paper is to explain the new nomothetic network psychiatry (NNP) approach, which uses machine learning methods to build data-driven causal models of mental illness by assembling risk-resilience, adverse outcome pathways (AOP), cognitome, brainome, staging, symptomatome, and phenomenome latent scores in a causal model. The latter may be trained, tested and validated with Partial Least Squares analysis. This approach not only allows to compute pathway-phenotypes or biosignatures, but also to construct reliable and replicable nomothetic networks, which are, therefore, generalizable as disease models. After integrating the validated feature vectors into a well-fitting nomothetic network, clustering analysis may be applied on the latent variable scores of the R/R, AOP, cognitome, brainome, and phenome latent vectors. This pattern recognition method may expose new (transdiagnostic) classes of patients which if cross-validated in independent samples may constitute new (transdiagnostic) nosological categories.