Stop Soldier Suicide + Mission Daybreak

Despite massive investments and decades of effort, modern Veteran suicide prevention practices fall short.

We have yet to truly understand the private, behind-the-walls details of Veterans’ lives during the last minutes, hours, days, and weeks before they die by suicide.

Epidemiological data are strong, but they don’t give us enough information about immediate individual risk. When we try to understand individual risk retrospectively, current psychological autopsies represent the limited perspective of those around the Veteran.

To date, we have never been able to capture the inner thoughts, feelings, and behaviors of those who died by suicide in the time immediately leading up to the event.

Without this individual-level, real-world data, current prevention efforts will continue to fall short of the ability to save more Veterans at risk.

Redefining the way we understand and act on risk.

Today, we share more intimate information with our digital devices than with anyone else in our lives. Our devices capture what we say, do and how we interact in both the digital and physical worlds.

At Stop Soldier Suicide, we believe this information can speak volumes about suicide risk and may uncover acute details in the days, weeks, and months before a death.

Modeled after the “black box” flight recorder used in aviation, our solution, Black Box Project, will conduct digital autopsies for Veterans who have died by suicide to recreate the final moments of life.

We’ll uncover never-before-known insights to completely redefine the way we understand signals of risk, and we’ll advance methods of outreach and care for Veterans at highest probability for suicide.

Digital Autopsies > Machine Learning > Actionable Insights.

Our innovation, Black Box Project, uses best-in-class forensic tools to extract data from digital devices entrusted to us by surviving family members of Veterans who died by suicide.*

With this extracted data, we’re developing:

Machine learning models to predict both who and when for identifying Veterans at greatest risk for suicide

The ability to find signals that no one else has identified and to share this model with the Veteran-serving community to save lives at scale

New persona models, look-alike profiles, and audience targeting strategies to reimagine high-risk outreach

A software development kit that can plug into multiple mental health or wellness apps and prompt just-in-time interventions

*After data extraction, devices are returned intact to survivors.

We leverage a diverse suite of Amazon Web Services (AWS) tools to unlock the potential of Black Box Project. Products such as Amazon Simple Storage Service (S3), AWS Athena, AWS SageMaker, and AWS Comprehend turn potentially disparate extraction and analysis processes into a streamlined tool with a consistent technology profile.

Machine learning algorithms, natural language processing, and entity extraction techniques – powered by AWS – are used to build models of pre-suicidal behaviors highly correlated with suicide, which uncover novel signals and insights that can be shared with the veteran-serving community to save lives at scale.

Committed to upholding the highest ethics in all we do.

Compassionate, Veteran-centered interactions with families of Veterans who have died by suicide
Ongoing donor consent obtained with protocols to protect device donors’ emotional safety
Strict safety protocols for securing and storing data collected, reviewed by a Data Safety Monitoring Board
De-identification of data and protecting sensitive information through a restricted-access dataset
Equity in generating risk predictions by Veteran sub-groups and data types
Privacy protection for living clients by running analyses locally on phones not connected to the internet