Differences in syntactic and semantic analysis based on machine learning algorithms in prodromal psychosis and normal adolescents

Schizophrenia has the main symptom of psychosis which is characterized by speech incoherence due to thought process disturbance. Before schizophrenia, there is a prodromal phase of psychosis in adolescence. Early recognition of this phase is important to prevent the development of symptoms into a severe mental disorder. Machine learning technology can be used to predict thought process disturbance through syntactic and semantic analysis of speech. This study aims to describe the differences in syntactic and semantic analysis in prodromal psychosis and normal adolescents. The research subjects consisted of 70 adolescents aged 14-19 years which were divided into 2 groups. Based on the results of the Prodromal Questionnaire-Brief (PQ-B) Indonesian version, the subjects were split into two groups: prodromal and normal. All participants were voice-recorded during interviews using an open-ended qualitative questionnaire. Syntactic and semantic analysis was carried out on all data which amounted to 1017 phrase segments and classified by machine learning. This is the first study in Indonesia to compare the analysis of syntactic and semantic aspects in prodromal psychosis and normal adolescent populations. There were significant differences in syntactic and semantic analysis between groups of adolescents with prodromal psychosis and normal adolescents at the minimum value of coherence and frequency of use of nouns, personal pronouns, subordinate conjunctions, adjectives, prepositions, and proper nouns.

Automated summary

This study aims to describe the differences in syntactic and semantic analysis in prodromal psychosis and normal adolescents using machine learning technology. Early recognition of the prodromal phase is important to prevent the development of severe mental disorder symptoms.