Adolescent suicidal risk assessment in clinician-patient interaction: A study of verbal and acoustic behaviors

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

“…The Suicidal Adolescent Clinical Trial (Pestian et al., ), the single‐site precursor to this study, which used machine learning to analyze interviews with 60 suicidal and control patients, classified patients into suicidal or control groups with greater than 90% accuracy (Pestian et al., ). Analysis of acoustic features such as pauses and vowel spacing yielded similar results (Scherer, Morency, Gratch, Pestian, & Playa Vista, ; Venek, Scherer, Morency, Rizzo, & Pestian, ). The study described herein is novel because it uses a multisite, multicultural setting to show that machine learning algorithms can be trained to automatically identify the suicidal subjects in a group of suicidal, mentally ill, and control subjects.…”

“…Moreover, the acoustic features did not play a substantial role in the initial study interview. Subsequent research, however, has shown that in some cases the acoustic features are statistically important during follow‐up visits (Venek, Scherer, Morency, Rizzo, & Pestian, ; Venek et al., ). From the results of other studies, this was unexpected.…”

Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally Sensitive Markers of Suicide Risk

“…Second, prior research has identified depressed affect (Bulik, Carpenter, Kupfer, & Frank, 1990), hopelessness (Hawton, Casanas, Haw, & Saunders, 2013; Smith, Alloy, & Abramson, 2006), and anxiety (Nock, Deming, et al, 2012) as important risk factors for suicide, which suggests that individuals at suicide risk may use language expressing these emotions at higher rates and with greater negative valence. In support of this idea, use of positive and negative emotion words in transcribed verbal interviews were significantly different among suicidal adolescent inpatients compared with control participants (Venek et al, 2014). In our study, we tested whether attempt episodes demonstrated significantly greater use of negative emotion words and less use of positive emotion words (as indicators of negative sentiment).…”

“…Given the large effect sizes reported in a number of studies examining some of the same LIWC variables, such as self-focus ( d s = 1.06–1.31; Stirman & Pennebaker, 2001; Venek et al, 2014), sentiment ( d s = 0.88–1.21; Venek et al, 2014), and constructs related to social engagement, such as belongingness ( d = 1.52; Braithwaite et al, 2016), we conducted a power analysis that was based on the assumption of large effect sizes (although no prior studies have examined within-subjects differences, which may or may not vary substantially from between-subjects comparisons). We determined that a sample size of 30 suicide attempters (representing 60 attempts but only 20 with collected, usable SMS data, divided by the design effect) would provide enough power to detect only large effect sizes (Cramer’s V = 0.29) for χ 2 tests of mixed-effects models comparing suicide attempts with other types of episodes, assuming 80% power and a significance level of .05.…”

“…One study found that poets who completed suicide as opposed to did not complete suicide relied on first-person pronouns to a much greater extent (Stirman & Pennebaker, 2001). Furthermore, in transcribed verbal interviews with suicidal and control adolescent inpatients, use of first-person pronouns was significantly higher for suicidal participants compared with control participants (Venek, Scherer, Morency, Rizzo, & Pestian, 2014). Lastly, first-person pronoun use was associated with a transition from a general mental health forum to a suicide forum among users of a popular online forum (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016).…”