Projects

​

​

Linguistic Markers of Mental State and Status

​

​

The goal of this line of research is to apply natural language processing

techniques (notably large language models) to detect clinically actionable

changes in mental state and status. Current application areas include

speech-based detection of deterioration in dementia; detection of cognitive

distortions; persecutory ideation and thought disorganization in data from

participants experiencing symptoms of psychosis; and detection of suicide risk

in search log histories contributed by participants. The long-term objective of

this work is to develop tools to support clinicians by enhancing their ability to

identify and act upon identified linguistic indicators.

​

​

​

Cognitive Behavioral Therapy for psychosis (CBTPro):

​

Psychosis resulting from schizophrenia spectrum disorders and major mood disorders is one of the most disabling health concerns worldwide. Evidence-based psychotherapeutic interventions are recommended as standard of care by national psychosis treatment guidelines but are rarely accessible. Cognitive Behavioral Therapy for psychosis (CBTp) is the most well-researched psychotherapy for psychotic disorders, yet fewer than 1% of American mental health providers are trained in this intervention. To date, there has been no scalable way to offer high-quality and sustainable training to mental health providers in CBTp-informed care. 

 

BRiTE faculty have partnered with small business Lyssn.io to systematically develop, pilot, and rigorously test CBTpro, a novel Computerized Clinician Support Tool that will use cutting-edge spoken language technology to provide immediate feedback and coaching to mental health staff and trainees who wish to learn CBTp. CBTpro will provide a rapid means of scaling and sustaining CBTp in routine care settings across the US, resulting in more clinicians  across the country providing higher quality CBTp to individuals with psychosis.

 

This work is supported by a National Institute of Mental Health (NIMH) Fast-Track Small Business Technology Transfer grant (R42MH123215-01). For more information about this project, please visit our website at: https://uwspiritlab.org/cbtpro/.

​

Automated Analysis of Speech and Language
 

Many of the cardinal symptoms of mental health conditions are detected in spoken language. For example, disordered thinking may manifest as speech that appears incoherent, lacking meaningful connections between phrases or sentences. Changes in mood may be expressed explicitly in language, or detectable from subtle linguistic indicators.

 

This project aims to develop and evaluate automated methods that detect linguistic indicators of clinically important changes in mental state, with the overarching goals of developing instruments to support continuous monitoring, and advancing scientific understanding of the transdiagnostic spectrum of symptomatology in psychiatry.