Pestian Lab group has conducted two prospective studies to understand the acoustic,
linguistic, facial expression, and genetic features of suicidal patients and
patients with mental illness. The goal
of the first study, the Suicidal Adolescent Clinical Trial (ACT) conducted in
2011 was to build a machine learning classifier to differentiate suicidal and
control adolescent patients using their linguistic and vocal
characteristics. The ongoing STM (Suicide
Thought Marker) study is an expanded version of the ACT study, where the main
goal is to build a machine learning classifier to differentiate suicidal
patients, patients with mental illness, and control patients using their
linguistic, vocal, genetic and/or facial expression characteristics.
both studies, machine learning techniques have been used to determine suicidal
risk using linguistic and acoustic features.
Ongoing analyses of the STM will determine the power of facial
expression and genetic features in complementing the assessment of suicidal
risk and mental illness.
learning classifiers were built using either linguistic features or acoustic
features from the ACT data set. These
classifiers were able to differentiate between suicidal and control patients
with at least 90% accuracy. Publications
related to the STM data are in progress, but comparable accuracies are
expected; although the STM cohort is more diverse in age range, demographics,
and variety of mental disorders, the cohort is larger.