Schizophrenia comprises various symptom domains the two most important being positive
and negative symptoms. Nevertheless, using (un)supervised machine learning techniques it was
shown that a) negative symptoms are significantly interrelated with PHEM (psychosis, hostility,
excitation, and mannerism) symptoms, formal thought disorders (FTD) and psychomotor
retardation (PMR); and b) stable phase schizophrenia comprises two distinct classes, namely Major
Neuro-Cognitive Psychosis (MNP, largely overlapping with deficit schizophrenia) and Simple
NP (SNP). In this study, we recruited 120 MNP patients and 54 healthy subjects and measured the
above-mentioned symptom domains. In MNP, there were significant associations between
negative and PHEM symptoms, FTD and PMR. A single latent trait, which is essentially
unidimensional, underlies these key domains of schizophrenia and additionally shows excellent
internal consistency reliability, convergent validity, and predictive relevance. Confirmatory Tedrad
Analysis indicates that this latent vector fits a reflective model. Soft Independent Modeling of
Class Analogy (SIMCA) shows that MNP (diagnosis based on negative symptoms) is better
modeled with PHEM symptoms, FTD and PMR than with negative symptoms. In conclusion, in
MNP, a restricted sample of the schizophrenia population, negative and PHEM symptoms, FTD
and PMR belong to one underlying latent vector reflecting general psychopathology and, therefore,
may be used as an overall severity of schizophrenia (OSOS) index. The bi-dimensional concept of
positive and negative symptoms and type I and II schizophrenia is revised.