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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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SPEECH MARKERS FOR CLINICAL ASSESSMENT OF COCAINE USERS
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Abstract:
One of the main foci of addiction research is the delineation of markers that track the propensity of relapse. Speech analysis can provide an unbiased assessment that can be deployed outside the lab, enabling objective measurements and relapse susceptibility tracking. This work is the first attempt to study unscripted speech markers in cocaine users. We analyzed 23 subjects performing two tasks: describing the positive consequences (PC) of abstinence and the negative consequences (NC) of using cocaine. We perform two main experiments: first, we analyzed whether acoustic and semantic features can infer clinical variables such as the Cocaine Selective Severity Assessment; then, we analyzed the main problem of interest: to see if these features are powerful enough to infer if the subjects remains abstinent. Our results show that speech features have potential to be used as a proxy to monitor cocaine users under treatment to recover from their addiction.
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URL: http://www.ncbi.nlm.nih.gov/pubmed/32095118 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7039659/ https://doi.org/10.1109/icassp.2019.8682691
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S19. ANALYZING NEGATIVE SYMPTOMS AND LANGUAGE IN YOUTHS AT RISK FOR PSYCHOSIS USING AUTOMATED LANGUAGE ANALYSIS
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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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