Source:
https://www.newsgram.com/ai-tool-detect-changes-mental-health-patients
https://www.newsgram.com/ai-tool-detect-changes-mental-health-patients
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.
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.
ccording to the study, the AI was able to match clinicians’
ratings of their patients.
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.
his 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. (IANS).
