Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
As 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.
Recommended Citation
Stemmer, Maya; Barzilay, Shira; Efrati, Itamar; Friedman, Talia; Carmi, Lior; Zohar, Mishael; Brunstein Klomek, Anat; Apter, Alan; and Fine, Shai, "Predicting Adolescent Suicide Risk From Cellphone Usage Data and Self-Report Assessments" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 7.
https://aisel.aisnet.org/hicss-57/hc/wellness_management/7
Predicting Adolescent Suicide Risk From Cellphone Usage Data and Self-Report Assessments
Hilton Hawaiian Village, Honolulu, Hawaii
As 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.
https://aisel.aisnet.org/hicss-57/hc/wellness_management/7