ArticleTranslational psychiatry2021
Natural Language Processing markers in first episode psychosis and people at clinical high-risk.
Article in Translational psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06978803 (HYEEG Discourse in Psychosis), which is not on this map. Cited by 47 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
HYEEG Discourse in Psychosis: A Neurobehavioural Study
Who cites it
47 citing papers in PubMed, 83 citations in OpenAlex.
- Measuring Depression Severity With Clinical Global Impression-Severity Scale Scores From Clinical Notes Using Large Language Models: Validation Study.JMIR formative 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
- An integrative NLP framework identifies multilevel linguistic phenotypes of schizophrenia across tasks.Psychological medicine · 2026Article
- TOPSY: A picture description dataset to examine speech, language and communication in untreated first episode psychosis.Data in brief · 2026Article
- Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Subjective language disturbances in young patients at ultra-high risk for psychosis (UHR): what relevance for clinical prognosis? A 2-year follow-up study.European archives of psychiatry and clinical neuroscience · 2026Article
- The DISCOURSE in psychosis (London Ontario): A speech dataset to examine communication disturbances in early-stage psychosis.Data in brief · 2026Article
- 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
- Three dimensions of speech coherence in people with early psychosis and their family members.Schizophrenia (Heidelberg, Germany) · 2025Article
- Syntactic network analysis in first-episode psychosis: toward generalizability.Schizophrenia (Heidelberg, Germany) · 2025Article
- Collecting language, speech acoustics, and facial expression to predict psychosis and other clinical outcomes: strategies from the AMP® SCZ initiative.Schizophrenia (Heidelberg, Germany) · 2025Article
- Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.Translational psychiatry · 2025Article
- Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior.Journal of psychiatric research · 2025Article
- Relationship between grammar and schizophrenia: a systematic review and meta-analysis.Communications medicine · 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
- Towards a latent space cartography of subjective experience in mental health.Psychiatry and clinical neurosciences · 2025Article
- Text mining of outpatient narrative notes to predict the risk of psychiatric hospitalization.Translational psychiatry · 2025Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent work has suggested that disorganised speech might be a powerful predictor of later psychotic illness in clinical high risk subjects. To that end, several automated measures to quantify disorganisation of transcribed speech have been proposed. However, it remains unclear which measures are most strongly associated with psychosis, how different measures are related to each other and what the best strategies are to collect speech data from participants. Here, we assessed whether twelve automated Natural Language Processing markers could differentiate transcribed speech excerpts from subjects at clinical high risk for psychosis, first episode psychosis patients and healthy control subjects (total N = 54). In-line with previous work, several measures showed significant differences between groups, including semantic coherence, speech graph connectivity and a measure of whether speech was on-topic, the latter of which outperformed the related measure of tangentiality. Most NLP measures examined were only weakly related to each other, suggesting they provide complementary information. Finally, we compared the ability of transcribed speech generated using different tasks to differentiate the groups. Speech generated from picture descriptions of the Thematic Apperception Test and a story re-telling task outperformed free speech, suggesting that choice of speech generation method may be an important consideration. Overall, quantitative speech markers represent a promising direction for future clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.