Predicting Adolescent Suicide Risk From Cellphone Usage Data and Self-Report Assessments

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dc.contributor.authorStemmer, Maya
dc.contributor.authorBarzilay, Shira
dc.contributor.authorEfrati, Itamar
dc.contributor.authorFriedman, Talia
dc.contributor.authorCarmi, Lior
dc.contributor.authorZohar , Mishael
dc.contributor.authorBrunstein Klomek, Anat
dc.contributor.authorApter, Alan
dc.contributor.authorFine, Shai
dc.date.accessioned2023-12-26T18:41:50Z
dc.date.available2023-12-26T18:41:50Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.441
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other44889881-41ee-41df-a34a-463f683fc72f
dc.identifier.urihttps://hdl.handle.net/10125/106824
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPersonal Health Management with Digital Solutions
dc.subjectabnormal behavior detection
dc.subjectdigital monitoring.
dc.subjectmachine learning
dc.subjectsuicide prediction
dc.titlePredicting Adolescent Suicide Risk From Cellphone Usage Data and Self-Report Assessments
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractAs suicide is a leading cause of adolescent death, innovative evaluation of imminent suicide risk factors is needed. This study followed high-risk adolescents who presented with recent suicidal thoughts and behaviors (STB) for six months. They were digitally monitored and periodically observed during in-clinic visits. We aimed to classify their STB levels and identify severe cases based on two types of digital monitoring: (1) weekly self-reported questionnaires by patients and (2) and continuously collected cellphone use data. We present a novel approach for utilizing the immense amounts of unlabeled cellular logs in a supervised classification problem. Satisfying prediction results from both data types showed the feasibility of using digital monitoring for STB prediction. Such a capability may enrich periodic clinical assessments with frequent digital follow-ups and raise awareness whenever necessary.
dcterms.extent10 pages
prism.startingpage3656

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