Word to the Wise: Informing Clinical Decision Making for Psychosis Using Speech and Language Biomarkers-View Presentation

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In a comprehensive talk by Sunny Tang, an assistant professor of psychiatry specializing in technology's role in mental health at Zucker Hillside Hospital, several innovative methods for measuring speech and language were discussed. These methods are crucial in diagnosing mental health conditions, with a particular emphasis on psychosis. Tang detailed the intricacies of employing natural language processing (NLP), artificial intelligence, and machine learning tools to dissect speech patterns related to psychiatric conditions.<br /><br />The talk covered various means of quantifying speech, including acoustics, phonetics, and syntax. Tang presented methods like the Linguistic Inquiry and Word Count (LIWC), which employs human-rated dictionaries to assess speech for emotional and cognitive attributes. She also highlighted advancements in NLP that employ embeddings—a machine-learning technique for understanding language nuances beyond human interpretation.<br /><br />Moreover, Tang delved into using semantic embeddings for identifying speech disorganization patterns, representing thought processes through computational models. Case studies showcased how speech biomarkers could predict clinical outcomes like relapse in psychosis, emphasizing the potential for speech analysis in early detection and personalized treatment strategies.<br /><br />Further discussions included the challenges of incorporating medication effects into speech biomarker studies and the ethical considerations of deploying AI in clinical settings. Tang concluded by underscoring the promising future of speech and language biomarkers in mental health care, offering insights into personalized medicine, measurement-based care, and possible applications across various psychiatric and neurodegenerative disorders.

Keywords

Sunny Tang

psychiatry

mental health

psychosis

natural language processing

artificial intelligence

machine learning

speech biomarkers

semantic embeddings

personalized treatment

ethical considerations