A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial. Issue 1 (3rd November 2016)

• Record Type: Journal Article
• Authors: Pestian, John P.; Sorter, Michael; Connolly, Brian; Bretonnel Cohen, Kevin; McCullumsmith, Cheryl; Gee, Jeffry T.; Morency, Louis‐Philippe; Scherer, Stefan; Rohlfs, Lesley
• Is Part Of: Suicide and life-threatening behavior. Volume 47:Issue 1(2017:Feb.)
• Page Start: 112
• Page End: 121
• Publication Date: 2016-11-03
• Subjects: Suicide -- Periodicals; Suicide -- Prevention -- Periodicals; Behavior -- Periodicals; Suicide -- Periodicals; 362.28
• DOI: 10.1111/sltb.12312
• Languages: English
• ISSNs: 0363-0234

Abstract

Abstract : Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers. In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal. Machine learning algorithms were used with the subjects' words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.