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Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach
1Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Gangdong-gu, South Korea.
Science Progress
|January 23, 2025
Summary
Language analysis using AI models revealed significant differences in sentence acceptability and semantic networks between schizophrenia and bipolar disorder patients, potentially aiding in differential diagnosis.
Area of Science:
- Psychiatry
- Computational Linguistics
- Neuroscience
Background:
- The diagnostic distinction between schizophrenia and bipolar disorder remains challenging due to overlapping symptoms and nosological ambiguity.
- Formal thought disorder has historically been a key feature differentiating schizophrenia, suggesting language alterations may be crucial.
- Investigating language as a potential biomarker could refine differential diagnosis in psychiatry.
Purpose of the Study:
- To explore the utility of natural language processing (NLP) in differentiating schizophrenia from bipolar disorder.
- To identify distinct language abnormalities in patients with schizophrenia and bipolar disorder.
- To assess if linguistic markers can serve as surrogate indicators for thought disorders.
Main Methods:
- Employed KoBERT and KoGPT language models to evaluate sentence acceptability in patients and controls.
- Constructed semantic word networks for each group to analyze linguistic structures.
- Compared network measures, including average clustering coefficient, between patient groups and controls.
Main Results:
- Both schizophrenia and bipolar disorder groups exhibited lower sentence acceptability compared to controls.
- The schizophrenia group demonstrated significantly less acceptable sentences than the bipolar disorder group.
- Distinct differences were observed in semantic network structures between schizophrenia and bipolar disorder, with bipolar disorder falling between schizophrenia and controls in most measures.
Conclusions:
- Significant differences in sentence acceptability and semantic network metrics were found between schizophrenia and bipolar disorder patients.
- Language abnormalities identified through NLP may serve as valuable surrogate markers for thought disorders.
- These linguistic markers show promise in improving the differential diagnosis between schizophrenia and bipolar disorder.
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