ArticleFrontiers in psychiatry2023
Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Interpretable LLM-Based Detection of Loose Associations Using Synthetic Speech Data in Early Psychosis.Schizophrenia bulletin · 2026Article
- Application of artificial intelligence in schizophrenia rehabilitation management: a systematic scoping review.Translational psychiatry · 2026Article
- Reduced linguistic coherence in psychosis defies semantic similarity accounts and relates to altered large-scale cortical hierarchy.Scientific reports · 2026Article
- Expressive writing combined with digital cognitive therapy in patients with schizophrenia: a narrative review of efficacy, linguistic phenotypes, and adherence modulators.Frontiers in psychiatry · 2026Review
- Perplexity and proximity: Large language model perplexity complements semantic distance metrics for the detection of incoherent speech.Journal of biomedical informatics · 2025Article
- Emotional tone in clinical high risk for psychosis: novel insights from a natural language analysis approach.Frontiers in psychiatry · 2024Article
- Linguistic findings in persons with schizophrenia-a review of the current literature.Frontiers in psychology · 2023Review
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8 authors.
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Abstract
Background: Impairments in speech production are a core symptom of non-affective psychosis (NAP). While traditional clinical ratings of patients' speech involve a subjective human factor, modern methods of natural language processing (NLP) promise an automatic and objective way of analyzing patients' speech. This study aimed to validate NLP methods for analyzing speech production in NAP patients. Methods: Speech samples from patients with a diagnosis of schizophrenia or schizoaffective disorder were obtained at two measurement points, 6 months apart. Out of Results: Results did not show differences for global vs. local coherence models and found more significant correlations between word2vec models and clinically relevant outcome variables than for GloVe models. Exploratory analysis of longitudinal data did not yield significant correlation with coherence scores. Conclusion: These results indicate that natural language processing methods need to be critically validated in more studies and carefully selected before clinical application.
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