Leveraging ambulatory assessment data and machine learning to develop personalized prediction models of suicidal ideation

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A.7. PROJECT SUMMARY/ABSTRACT The proposed research will leverage active and passive ambulatory assessment (AA) methods and machine learning to develop personalized suicidal ideation (SI) prediction models among a clinical sample of youth at high-risk for suicidality. This work is timely and important, given that suicide is currently the second-leading cause of death among youth in the U.S., and rates of SI and suicidal behavior have risen steadily over the past 20 years. SI is a critical target for predictive models, as it is an identifiable, reliable, and modifiable antecedent of suicidal behavior. Developing predictive models that can effectively predict SI may pave the way for just-in- time interventions delivered at the precise time of peak risk, thus preventing suicidal behavior before it occurs. However, despite decades of research, our ability to accurately predict SI remains poor, likely because suicidality results from a complex interaction between contextualized dynamic processes that are largely specific to each individual. Yet, most research has attempted to predict SI, a highly person-specific phenomenon, from group-level data—which can adequately identify who is at risk but not when an individual is at risk, which is critical for prevention. Thus, to improve our understanding and prediction of SI it is imperative to take an approach that properly accounts for individual variability (i.e., personalized or precision medicine), whereby the model is fit to the person rather than vice versa. Advancements in ambulatory assessment, mobile computing, and machine learning allow for the collection, management, and analysis of dynamic, high-resolution data required to develop personalized risk prediction models. I propose to combine these methodological advancements to develop personalized models of SI prediction. Specifically, among a population of youth at high-risk for suicidality, this study will use ecological momentary assessment (EMA) to assess SI severity twice daily and collect continuous passive sensor data from smartphones for 100 consecutive days. This project will map passively collected sensor data onto variables that are empirically and theoretically linked to suicide risk, such as physical activity and mobility, communication, and social interaction. This combination of daily ratings of SI severity and continuous passive sensor data will provide the necessary data to develop personalized risk calculators that model each person’s variability in SI severity as a function of passive sensor data. This study will further current conceptualizations of suicide risk and prediction using advanced methodological and computational approaches, and provide training in innovative methods that have the potential to predict SI risk in real-time, which is responsive to Objectives 2.2 and 4.1 of NIMH’s Strategic Plan and holds tremendous promise for improving suicide prevention efforts.