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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study
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In: Journal of Medical Internet Research (2020)
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Using language processing and speech analysis for the identification of psychosis and other disorders
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In: Biol Psychiatry Cogn Neurosci Neuroimaging (2020)
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Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion
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In: PLoS One (2020)
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Identifying signals associated with psychiatric illness utilizing language and images posted to Facebook
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In: NPJ Schizophr (2020)
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Language as a Biomarker for Psychosis: A Natural Language Processing Approach
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In: Schizophr Res (2020)
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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study
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In: J Med Internet Res (2020)
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Linguistic markers predict onset of Alzheimer's disease
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In: EClinicalMedicine (2020)
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Detection of acute 3,4-methylenedioxymethamphetamine (MDMA) effects across protocols using automated natural language processing
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From discourse to pathology: Automatic identification of Parkinson’s disease patients via morphological measures across three languages
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In: Cortex (2020)
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The history of writing reflects the effects of education on discourse structure: implications for literacy, orality, psychosis and the axial age
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Differential 28-Days Cyclic Modulation of Affective Intensity in Female and Male Participants via Social Media
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S19. ANALYZING NEGATIVE SYMPTOMS AND LANGUAGE IN YOUTHS AT RISK FOR PSYCHOSIS USING AUTOMATED LANGUAGE ANALYSIS
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Abstract:
BACKGROUND: Schizophrenia is characterized by disturbances in thought and language, often resulting in reduced production and complexity of speech, and abnormal pauses; these language disturbances have been associated with negative symptoms (Cohen et al., 2016). Using clinical ratings, we and others have shown that both language disturbances and negative symptoms are evident prior to psychosis onset in youths at clinical high risk (CHR). Automated natural language processing (NLP) analyses of transcribed speech show that both reductions in semantic coherence and syntactic complexity predict psychosis onset in CHR individuals (Bedi et al., 2015). In the current study, we assessed whether NLP syntactic complexity features and aberrant pauses may be associated with negative symptoms in CHR youths. METHODS: Participants included 33 CHR youths (mean (SD) age 21 (4) years; 11 females; ethnically diverse), of whom 5 developed psychosis (CHR+) within 2 years, whereas 28 did not (CHR-). Speech was elicited using open-ended interview. Audio files were transcribed and de-identified; using the Natural Language Toolkit (www.nltk.org), these transcripts were subjected to preprocessing (e.g. lemmatized) and analyzed using latent semantic analysis and part-of-speech tagging to characterize syntax. PRAAT (www.fon.hum.uva.nl/praat) was used for the analysis of pauses in audio files. Based on the criterion set forth by Goldman-Eisler (1968), pauses were defined as any silence longer than 250ms, as pauses less than 250ms are considered to signify breathing and articulation, while pauses longer than 250ms are assumed to reflect higher level cognitive processes, such as planning, or pathological phenomena, such as thought blocking. Mean pause length was calculated, as well as the percentage of time spent during the encounter spent in silences greater than 250ms, defined as the percentage of pauses. Negative symptoms were assessed using the Structured Interview for Psychosis-Risk Syndromes (SIPS). Spearman correlational analyses of demographics, linguistic variables and negative symptoms were performed. RESULTS: CHR patients had a mean (SD) total negative symptom score of 13.42(7.77). For pause features, patients had a mean pause length of 1.1 (0.5) seconds, with 50% (SD 17%) percentage of pauses. Total negative symptom severity was significantly associated with mean pause length (r=.50, p<.005), percentage of pauses (r=.56, p<.005), and phrase length (r=-.51, p<.005), which were all intercorrelated (all r’s> 0.4). Negative symptoms were also associated with the use of determiner pronouns such as “which” and “that”, which introduce dependent clauses (r=-.38, p<.05), which was unrelated to the other features. None of the variables of interest were associated with age or gender. Specific SIPS items that drove these associations included social anhedonia (N1) and decreased expression of emotion (N3). DISCUSSION: This is the first study to evaluate the association of NLP syntactic variables and auditory features in speech with negative symptoms in a clinical high-risk cohort. Consistent with studies in schizophrenia, reduced syntactic complexity and aberrant use of pauses were associated with negative symptoms, primarily social anhedonia and decreased expression of emotion. This study describes potential early linguistic markers of negative symptoms and psychosis risk that could be used for early identification and provide targets for early preventive intervention. Further study is needed in larger cohorts, as well as mechanistic studies to understand these language impairments in the context of pathophysiological theories of schizophrenia development.
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Keyword:
Poster Session III
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6455321/
https://doi.org/10.1093/schbul/sbz020.564
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| 15 |
24.2 NATURAL LANGUAGE PROCESSING STUDIES OF PSYCHOSIS AND ITS RISK STATES
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Prediction of psychosis across protocols and risk cohorts using automated language analysis
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The maturation of speech structure in psychosis is resistant to formal education
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Predicting natural language descriptions of mono-molecular odorants
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The maturation of speech structure in psychosis is resistant to formal education
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The ontogeny of discourse structure mimics the development of literature ...
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