The Significance of Natural Language Processing and Machine Learning in Schizophasia Description. Identification of Research Trends and Perspectives in Schizophrenia Language Studies

Abstract

Introduction: Language and speech serve as significant biomarkers for psychiatric disorders, including schizophrenia. The linguistic features associated with schizophasia have been a focal point since the early descriptions of schizophrenia. Over the past twenty-five years, scientific reflection on language in mental illnesses has dynamically provided new data identifying the complex phenomenon of speech pathology in schizophrenia. Material and methods: A bibliometric analysis was conducted using SCOPUS data, focusing on word co-occurrence patterns in schizophrenia research. VOSviewer was employed for visualization, and semantic relationships between words were explored. Results: An analysis has revealed trends and gaps in research on schizophasia. Integrating temporal and spatial visualizations of metadata has allowed for the identification of currently employed measures of incoherence in schizophatic texts across various levels of linguistic organization. Keyword modeling has demonstrated a growing interest in utilizing artificial intelligence techniques to develop linguistic biomarkers for schizophrenia and other mental disorders. Conclusions: The harmonization of computational methods for measuring narrative, dialogic, and prosodic coherence holds promise, particularly in cross-validation studies involving other neuroindicators of mental disorders. Developing linguistic biomarkers using broadly understood artificial intelligence requires multidisciplinary research teams integrating experts from psychiatry, neurolinguistics, neurologopedics, and AI engineering. Clear domain-specific regulations are essential to ensure accurate conclusions and ethical considerations. The study of schizophasia prospects is particularly evident at the lexical, semantic, and syntactic levels, along with affective and neurophysiological variables. Keywords: language, schizophasia, speech pathology, bibliomeric analysis, formal thought disorder