ReviewFrontiers in psychiatry2019
A Comprehensive Review of Computational Methods for Automatic Prediction of Schizophrenia With Insight Into Indigenous Populations.
Review in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- Improving Psychiatry Services with Artificial Intelligence: Opportunities and Challenges.Turk psikiyatri dergisi = Turkish journal of psychiatry · 2024Review
- Active Inference, Epistemic Value, and Uncertainty in Conceptual Disorganization in First-Episode Schizophrenia.Schizophrenia bulletin · 2023Article
- Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features.Schizophrenia bulletin · 2023Article
- Semantic Speech Networks Linked to Formal Thought Disorder in Early Psychosis.Schizophrenia bulletin · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation.Schizophrenia bulletin · 2022Article
- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
- High-Order Language Processing Difficulties in Patients With Schizophrenia: Cross-linguistic and Cross-cultural Results From the Hindi Version of a Newly Developed Language Test.Schizophrenia bulletin open · 2022Article
- Widespread cortical thinning, excessive glutamate and impaired linguistic functioning in schizophrenia: A cluster analytic approach.Frontiers in human neuroscience · 2022Article
- A word is worth a thousand pictures: A 20-year comparative analysis of aberrant abstraction in schizophrenia, affective psychosis, and non-psychotic depression.Schizophrenia research · 2021Article
- Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders.NPJ schizophrenia · 2021Article
- Baseline Cortical Thickness Reductions in Clinical High Risk for Psychosis: Brain Regions Associated with Conversion to Psychosis Versus Non-Conversion as Assessed at One-Year Follow-Up in the Shanghai-At-Risk-for-Psychosis (SHARP) Study.Schizophrenia bulletin · 2021Article
- Modeling Incoherent Discourse in Non-Affective Psychosis.Frontiers in psychiatry · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psychiatrists rely on language and speech behavior as one of the main clues in psychiatric diagnosis. Descriptive psychopathology and phenomenology form the basis of a common language used by psychiatrists to describe abnormal mental states. This conventional technique of clinical observation informed early studies on disturbances of thought form, speech, and language observed in psychosis and schizophrenia. These findings resulted in language models that were used as tools in psychosis research that concerned itself with the links between formal thought disorder and language disturbances observed in schizophrenia. The end result was the development of clinical rating scales measuring severity of disturbances in speech, language, and thought form. However, these linguistic measures do not fully capture the richness of human discourse and are time-consuming and subjective when measured against psychometric rating scales. These linguistic measures have not considered the influence of culture on psychopathology. With recent advances in computational sciences, we have seen a re-emergence of novel research using computing methods to analyze free speech for improving prediction and diagnosis of psychosis. Current studies on automated speech analysis examining for semantic incoherence are carried out based on natural language processing and acoustic analysis, which, in some studies, have been combined with machine learning approaches for classification and prediction purposes.
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