ArticleSchizophrenia (Heidelberg, Germany)2022
Automatic language analysis identifies and predicts schizophrenia in first-episode of psychosis.
Article in Schizophrenia (Heidelberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Ethics and Autonomy in Schizophrenia Care : Reflections from a Latin American Perspective.Journal of bioethical inquiry · 2026Article
- Linguistic Markers in At-Risk Mental States Using Natural Language Processing: A Systematic Review.Healthcare (Basel, Switzerland) · 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
- Collecting language, speech acoustics, and facial expression to predict psychosis and other clinical outcomes: strategies from the AMP® SCZ initiative.Schizophrenia (Heidelberg, Germany) · 2025Article
- Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.BMC psychiatry · 2025Article
- A single composite index of semantic behavior tracks symptoms of psychosis over time.Schizophrenia research · 2025Article
- A single composite index of semantic behavior tracks symptoms of psychosis over time.medRxiv : the preprint server for health sciences · 2025Article
- Detecting ADHD through natural language processing and stylometric analysis of adolescent narratives.Frontiers in child and adolescent psychiatry · 2025Article
- Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models.Frontiers in psychiatry · 2025Article
- Editorial: Natural language processing and artificial intelligence tools to explore the relationship between language and schizophrenia from diagnosis to care.Frontiers in psychiatry · 2025Article
- Approximating the semantic space: word embedding techniques in psychiatric speech analysis.Schizophrenia (Heidelberg, Germany) · 2024Article
- Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.The Permanente journal · 2024Article
- Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges.Journal of personalized medicine · 2024Article
- Language and communication rehabilitation in patients with schizophrenia: A narrative review.Heliyon · 2024Review
- Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features.Sensors (Basel, Switzerland) · 2022Article
- Deconstructing heterogeneity in schizophrenia through language: a semi-automated linguistic analysis and data-driven clustering approach.Schizophrenia (Heidelberg, Germany) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.
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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.