ArticleFrontiers in psychiatry2020
Modeling Incoherent Discourse in Non-Affective Psychosis.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.Neuropsychologia · 2026Article
- Interpretable LLM-Based Detection of Loose Associations Using Synthetic Speech Data in Early Psychosis.Schizophrenia bulletin · 2026Article
- Characterizing the spiral: potential mechanisms in AI-associated delusions.NPP - digital psychiatry and neuroscience · 2026Review
- Rethinking language, cognition and assessment in psychosis: How bilingualism challenges psychiatry and how natural language processing can help.Schizophrenia (Heidelberg, Germany) · 2026Review
- Expressive pragmatic language in mood and psychotic disorders: a systematic review and meta-analysis.Schizophrenia (Heidelberg, Germany) · 2026Review
- Quantifying improvement of psychotic symptoms in clozapine-treated schizophrenia: clinical note analysis with large language models.Scientific reports · 2026Article
- Perplexity and proximity: Large language model perplexity complements semantic distance metrics for the detection of incoherent speech.Journal of biomedical informatics · 2025Article
- Quantitative Research on Digitalized Treatment Options for Older Adults With Mental Illness: Scoping Review.JMIR mental health · 2025Article
- Transforming social media text into predictive tools for depression through AI: A test-case study on the Beck Depression Inventory-II.PLOS digital health · 2025Article
- Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder.... Workshop on Computational Linguistics and Clinical Psychology · 2025Article
- Coherence and comprehensibility: Large language models predict lay understanding of health-related content.Journal of biomedical informatics · 2025Article
- Approximating the semantic space: word embedding techniques in psychiatric speech analysis.Schizophrenia (Heidelberg, Germany) · 2024Article
- Level of structural integration in people with schizophrenia and schizoaffective disorders - applicability and associations with clinical parameters.Frontiers in psychiatry · 2024Article
- Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Computerized text and voice analysis of patients with chronic schizophrenia in art therapy.Scientific reports · 2023Article
- A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges.Bioengineering (Basel, Switzerland) · 2023Review
- Validation of natural language processing methods capturing semantic incoherence in the speech of patients with non-affective psychosis.Frontiers in psychiatry · 2023Article
- Fully automated detection of formal thought disorder with Time-series Augmented Representations for Detection of Incoherent Speech (TARDIS).Journal of biomedical informatics · 2022Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis.
methodsSpeech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects.
resultsCoherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder.
conclusionsAutomated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.
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