Language disorder as a marker for schizophrenia

Area of Science:

• Psychiatry
• Computational Linguistics
• Neuroscience

Background:

• Language disorders are a key symptom of schizophrenia, traditionally diagnosed through subjective patient interviews.
• Current diagnostic methods for schizophrenia rely heavily on verbal and non-verbal patient communication analysis.
• Language impairments in schizophrenia are often linked to cognitive deficits and thinking process disturbances.

Purpose of the Study:

• To introduce an objective, quantitative method for analyzing language disorders in schizophrenia.
• To propose novel biomarkers for schizophrenia based on language assessment.
• To explore the potential of Natural Language Processing (NLP) in understanding schizophrenia's semantic and pragmatic language aspects.

Main Methods:

• Utilizing Natural Language Processing (NLP) with semantic space models for computational language analysis.
• Analyzing language from phonetic to pragmatic levels.
• Developing objective and quantitative assessment tools for language disorders.

Main Results:

• NLP offers a groundbreaking approach to identifying biomarkers for schizophrenia.
• Computational analysis provides insights into semantic and pragmatic language processing in schizophrenia.
• Objective detection of language disorders is feasible with current technology.

Conclusions:

• Natural Language Processing (NLP) presents a novel, objective method for diagnosing and monitoring schizophrenia.
• Language disorder assessment via NLP can serve as a crucial tool for screening, prognosis, and therapy evaluation.
• This approach facilitates early interference detection, potentially from the prodromal phase of schizophrenia.