Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory

Abstract

Objective: Verbal communication contains key information for mental health assessment. Researchers have linked psychopathology phenomena to certain counterparts in natural language processing. We characterized subtle impairments in the early stages of psychosis, developing new analysis techniques, which led to a comprehensive map associating features of natural language processing with the full range of clinical presentation. Methods: We used natural language processing to assess spontaneous and elicited speech by 60 individuals with at-risk mental states and 73 controls who were screened from 4,500 quota-sampled Portuguese speaking residents of São Paulo, Brazil. Psychotic symptoms were independently assessed with the Structured Interview for Psychosis-Risk Syndromes. Speech features (e.g., sentiments and semantic coherence), including novel ones, were correlated with psychotic traits (Spearman’s-ρ) and at-risk mental state status (general linear models and machine-learning ensembles). Results: Natural language processing features were informative for classification, presenting a balanced accuracy of 86%. Features such as semantic laminarity (as perseveration), semantic recurrence time (as circumstantiality), and average centrality in word repetition graphs carried the most information and were directly correlated with psychotic symptoms. Grammatical tagging (e.g., use of adjectives) was the most relevant standard measure. Conclusion: Subtle speech impairments can be detected by sensitive methods and can be used in at-risk mental states screening. We have outlined a blueprint for speech-based evaluation, pairing features to standard psychometric items for thought disorder.

Keywords

• Psychosis
• at-risk mental states
• natural language processing
• semantics
• screening
• machine learning