Diagnosing Schizophrenia in the 21st Century: Natural Language Processing an Emerging Biomarker-View Presentation

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The session focused on the application of natural language processing (NLP) and computational speech analysis in diagnosing and monitoring schizophrenia. The panel, moderated by Dr. Marlon Danilewicz, comprised esteemed experts Dr. Sunny Tang, Dr. Justin Baker, and Dr. Michael Spilka. They discussed the burgeoning research in NLP as an emergent biomarker for psychiatric disorders, specifically schizophrenia.<br /><br />Dr. Sunny Tang highlighted the historical significance of speech as a symptom of psychiatric disorders, referencing Emil Kraepelin's early observations. Tang introduced the audience to computational speech analysis, emphasizing its role in sensitively capturing subclinical disturbances which traditional clinical ratings might miss. A pilot study she presented showed NLP features outperforming clinical ratings in diagnosing schizophrenia.<br /><br />Dr. Justin Baker elaborated on the use of multi-modal data from speech, video, and wearables to capture mental states. He demonstrated how computational analyses of verbal and non-verbal cues in psychiatric interviews could predict symptom severity. His research showed promising results in predicting both positive and negative symptoms of psychosis through linguistic markers.<br /><br />Dr. Michael Spilka focused on practical applications, hurdles, and future directions for integrating speech analysis in clinical care. He discussed a feasibility study in remote speech assessment for first-episode psychosis patients, revealing high retention and minimal technical issues, suggesting its potential in clinical practice.<br /><br />The session explored the ethical implications and barriers like privacy concerns, model biases, and the need for comprehensive data across diverse populations. The presenters encouraged continued innovation and cautious integration of these digital tools to enhance psychiatric diagnostics and patient care.

Keywords

natural language processing

NLP

computational speech analysis

schizophrenia

psychiatric disorders

biomarker

clinical ratings

multi-modal data

mental states

speech assessment

ethical implications

privacy concerns

psychiatric diagnostics