ArticleNPJ schizophrenia2019
A machine learning approach to predicting psychosis using semantic density and latent content analysis.
Article in NPJ schizophrenia, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 1 of them a synthesis that pooled it.
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Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Apical integration theory provides a neural basis for understanding disorganization and consciousness in the schizophrenias.Current research in neurobiology · 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
- 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
- Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.Molecular psychiatry · 2026Article
- On the interface between linguistics, computer science and psychiatry: analyzing textual key-factors affecting BERT-based classification of schizophrenia in social media texts.Frontiers in artificial intelligence · 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
- Multimodal machine learning for video based single question mental health assessment.NPJ digital medicine · 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
- Can artificial intelligence be the future solution to the enormous challenges and suffering caused by Schizophrenia?Schizophrenia (Heidelberg, Germany) · 2025Review
- Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.Translational psychiatry · 2025Article
- Tracking priming-induced language recovery in aphasia with pre-trained language models.Frontiers in artificial intelligence · 2025Article
- Automated speech and language markers of longitudinal changes in psychosis symptoms.NPP - digital psychiatry and neuroscience · 2025Article
- Developmental perspectives on the origins of psychotic disorders: The need for a transdiagnostic approach.Development and psychopathology · 2024Review
- Natural Language Processing Applied to Spontaneous Recall of Famous Faces Reveals Memory Dysfunction in Temporal Lobe Epilepsy Patients.bioRxiv : the preprint server for biology · 2024Article
- Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges.Journal of personalized medicine · 2024Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Subtle features in people's everyday language may harbor the signs of future mental illness. Machine learning offers an approach for the rapid and accurate extraction of these signs. Here we investigate two potential linguistic indicators of psychosis in 40 participants of the North American Prodrome Longitudinal Study. We demonstrate how the linguistic marker of semantic density can be obtained using the mathematical method of vector unpacking, a technique that decomposes the meaning of a sentence into its core ideas. We also demonstrate how the latent semantic content of an individual's speech can be extracted by contrasting it with the contents of conversations generated on social media, here 30,000 contributors to Reddit. The results revealed that conversion to psychosis is signaled by low semantic density and talk about voices and sounds. When combined, these two variables were able to predict the conversion with 93% accuracy in the training and 90% accuracy in the holdout datasets. The results point to a larger project in which automated analyses of language are used to forecast a broad range of mental disorders well in advance of their emergence.
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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.