Citation

Nobles AL, Glenn JJ, Kowsari K, Teachman BA, Barnes LE. Proc. SIGCHI Conf. Hum. Factor Comput. Syst. 2018; 2018(ePub): ePub.

Affiliation

Dept. of Systems and Information Engineering University of Virginia lb3dp@virginia.edu.

Copyright

(Copyright © 2018, Association for Computing Machinery)

DOI

10.1145/3173574.3173987

PMID

30944915

PMCID

PMC6442737

Abstract

Suicide is the second leading cause of death among young adults but the challenges of preventing suicide are significant because the signs often seem invisible. Research has shown that clinicians are not able to reliably predict when someone is at greatest risk. In this paper, we describe the design, collection, and analysis of text messages from individuals with a history of suicidal thoughts and behaviors to build a model to identify periods of suicidality (i.e., suicidal ideation and non-fatal suicide attempts). By reconstructing the timeline of recent suicidal behaviors through a retrospective clinical interview, this study utilizes a prospective research design to understand if text communications can predict periods of suicidality versus depression. Identifying subtle clues in communication indicating when someone is at heightened risk of a suicide attempt may allow for more effective prevention of suicide.

Language: en

Keywords

H.1.2 User/Machine Systems; I.5 Pattern Recognition; J.3 Life and Medical Sciences: Health; J.4 Social and Behavioral Sciences: Psychology; depression; mental health; social media; suicide; text messages