@article{ref1,
title="Using intensive longitudinal data to identify early predictors of suicide-related outcomes in high-risk adolescents: practical and conceptual considerations",
journal="Assessment",
year="2020",
author="Czyz, Ewa K. and Yap, Jamie R. T. and King, Cheryl A. and Nahum-Shani, Inbal",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Mobile technology offers new possibilities for assessing suicidal ideation and behavior in real- or near-real-time. It remains unclear how intensive longitudinal data can be used to identify proximal risk and inform clinical decision making. In this study of adolescent psychiatric inpatients (N = 32, aged 13-17 years, 75% female), we illustrate the application of a three-step process to identify early signs of suicide-related crises using daily diaries. Using receiver operating characteristic (ROC) curve analyses, we considered the utility of 12 features-constructed using means and variances of daily ratings for six risk factors over the first 2 weeks postdischarge (observations = 360)-in identifying a suicidal crisis 2 weeks later. Models derived from single risk factors had modest predictive accuracy (area under the ROC curve [AUC] 0.46-0.80) while nearly all models derived from combinations of risk factors produced higher accuracy (AUCs 0.80-0.91). Based on this illustration, we discuss implications for clinical decision making and future research.<p /> <p>Language: en</p>",
language="en",
issn="1073-1911",
doi="10.1177/1073191120939168",
url="http://dx.doi.org/10.1177/1073191120939168"
}