AI tool to detect changes in patients' mental health

Researchers, including one of Indian-origin, have developed an artificial intelligence (AI) tool that can accurately detect changes in clinical states in voice data of patients with bipolar, schizophrenia and depressive disorders as accurately as attending doctors.

"Machine learning allowed us to illuminate the various clinically-meaningful dimensions of language use and vocal patterns of the patients over time and personalised at each individual level," said Indian-origin researcher and study senior author Shri Narayanan from University of Southern California (USC) in the US.

The USC Signal Analysis and Interpretation Lab (SAIL), which has long applied artificial intelligence and machine learning to identify and classify video, audio and physiological data, partnered with researchers to analyse voice data from patients being treated for serious mental illnesses.

For the results, the researchers used the 'MyCoachConnect' interactive voice and mobile tool, created and hosted on the Chorus platform to provide voice diaries related to their mental health states.

SAIL team then collaborated with researchers to apply artificial intelligence to listen to hundreds of voicemails using custom software to detect changes in patients' clinical states.

According to the study, the AI was able to match clinicians' ratings of their patients.

Tracking changes in clinical states is important to detect if there is a change that shows whether the condition has improved or worsened that would warrant the need for changing treatment, the researchers said.

This project builds on SAIL's body of work in behavioural machine intelligence to analyse psychotherapy sessions to detect empathy of addiction counselors-in-training in order to improve their chances of better outcomes, in addition to the Lab's work analysing language for cognitive diagnoses and legal processes.

"Our approach builds on that fundamental technique to hear what people are saying about using the modern AI. We hope this will help us better understand how our patients are doing and transform mental health care to be more personalised and proactive to what an individual needs," said study lead author Armen Arevian.