Language encompasses acoustics, facial and body expressions, and linguistics. All these expressions are markers of a person’s thoughts. Measuring thought markers (Tm) is a challenge. We can’t use the same techniques we use with DNA and other biological markers.

Our partnership with the Oak Ridge National Laboratory in Tennessee gives us access to the nation’s most powerful computer systems. Together, we lead multiple studies that incorporate complex analyses from several datasets.

Our research has included multiple unique and impactful studies including:

Development of a suicide note database. Starting with a collection of 1,300 notes, we annotated, digitized and established vectors. We identified emotions in the language used and detected signals.

Determining classification signals in spoken words. We set out to prove that by using language, subjects can be classified as either suicidal or control. We established interview questions that led to subject responses with a 93% classifier accuracy.

Studying suicide thought markers (STM). We analyzed linguistics, acoustics, facial expressions and genetics data from 379 emergency department and outpatient subjects of all ages from three sites. Results showed that subjects can be classified into three groups: suicidal, mentally ill but not suicidal, or controls. This study reveals how advanced technology can be used for suicide assessment and prevention. For example, pauses in a person’s speech were longer for those identified as suicidal. Vowel spacing—the space a vowel takes up in a spoken word—is smaller in people with certain mental illness.