ArticleSchizophrenia bulletin2021
Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods.
Article in Schizophrenia bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation.Schizophrenia bulletin · 2023Pooled it
- ACES: ascertaining diagnosis classification with elicited speech in individuals with heterogeneous, comorbid psychiatric disorders.Psychiatry research · 2026Article
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Structural neuroimaging correlates of semantic incoherence in schizophrenia.European archives of psychiatry and clinical neuroscience · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.Molecular psychiatry · 2026Article
- Uncertainty modeling in multimodal speech analysis across the psychosis spectrum.NPJ digital medicine · 2026Article
- Tracking the dynamic breakdown of contextual coherence in schizophrenia using language models.Frontiers in psychiatry · 2026Article
- Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study.JMIR formative research · 2025Article
- Syntactic network analysis in first-episode psychosis: toward generalizability.Schizophrenia (Heidelberg, Germany) · 2025Article
- Automated speech and language markers of longitudinal changes in psychosis symptoms.NPP - digital psychiatry and neuroscience · 2025Article
- Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models.Frontiers in psychiatry · 2025Article
- Reduced speech coherence in psychosis-related social media forum posts.Schizophrenia (Heidelberg, Germany) · 2024Article
- Language switching and speaking a nondominant language challenge executive control: Preliminary data for novel behavioral markers of Alzheimer's risk in Spanish-English bilinguals.Neuropsychology · 2024Article
- Rationale and Challenges for a New Instrument for Remote Measurement of Negative Symptoms.Schizophrenia bulletin open · 2024Review
- Emotional tone in clinical high risk for psychosis: novel insights from a natural language analysis approach.Frontiers in psychiatry · 2024Article
- Article
- Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Neural evidence of switch processes during semantic and phonetic foraging in human memory.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Speech characteristics yield important clues about motor function: Speech variability in individuals at clinical high-risk for psychosis.Schizophrenia (Heidelberg, Germany) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
The language and speech of individuals with psychosis reflect their impairments in cognition and motor processes. These language disturbances can be used to identify individuals with and at high risk for psychosis, as well as help track and predict symptom progression, allowing for early intervention and improved outcomes. However, current methods of language assessment-manual annotations and/or clinical rating scales-are time intensive, expensive, subject to bias, and difficult to administer on a wide scale, limiting this area from reaching its full potential. Computational methods that can automatically perform linguistic analysis have started to be applied to this problem and could drastically improve our ability to use linguistic information clinically. In this article, we first review how these automated, computational methods work and how they have been applied to the field of psychosis. We show that across domains, these methods have captured differences between individuals with psychosis and healthy controls and can classify individuals with high accuracies, demonstrating the promise of these methods. We then consider the obstacles that need to be overcome before these methods can play a significant role in the clinical process and provide suggestions for how the field should address them. In particular, while much of the work thus far has focused on demonstrating the successes of these methods, we argue that a better understanding of when and why these models fail will be crucial toward ensuring these methods reach their potential in the field of psychosis.
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Registered trials
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