THE CHALLENGE: Predicting and Mitigating Veteran Suicide
Every day, 20 veterans commit suicide, according to estimates from the Veterans Administration (VA). The VA’s coordinated national strategy to address this public health issue focuses on high-risk individuals in health care settings and community engagement. The agency is also looking for innovative ways to prevent suicide for veterans that do not use VA health care.
Studies show that eight out of ten people considering suicide give some sign of their intentions. And, according to research published in the journals Crisis and
The American Journal of Public Health, real-time monitoring of online behavior can be a viable tool for assessing suicide risk factors on a large scale. This research also concludes that social media provides a channel that may allow others to intervene following an expression of suicidal thoughts online.
Open Source Intelligence (OS-INT)
is a deep-web listening tool that uses machine learning and artificial intelligence to assess and prioritize risk.
OS-INT scours publicly available data across the entire Internet, correlating names entered into the system with content related to 20 different risk factors, known as Behavioral Affinity Models (BAMs), and cross-referenced with more than 1 million queries into Lumina’s proprietary databases of risk.
Searches provide near-instant results, delivering meaningful, actionable intelligence to identify and prevent risk.
Internet Intelligence’s (NET-INT)
proprietary algorithms continuously identify, monitor, capture, and prioritize IP addresses exhibiting anomalous behavior across multiple risk dimensions.
The system also searches known IP addresses, producing all URLs accessed by the address. Results are nearly instantaneous, and accessed with just three clicks on the computer’s mouse.
The platform collects and stores more than 1 million interactions every day and since its inception has recorded more than 623,000 IP addresses engaged with threat-related risk topics.
Human Intelligence’s (HUM-INT) is powered by the S4 app, a crowd-sourced, mobile application that allows users to confidentially report concerns in real time. Almost 10% of the tips that have come into Lumina’s S4 platform have been related to suicide risk.
THE SOLUTION: Early Identification and Intervention through AI and Machine Learning
OS-INT, NET-INT and HUM-INT are a powerful tools that work in combination to help with early identification and intervention.
OS-INT’s suicide prevention bundles includes more 350 terms. OS-INT performs more than 7,240 searches across all publicly-available data on the web, correlating names with these terms and cross-referencing over 1 million queries into Lumina’s proprietary databases of risk. A manual web search of this magnitude would take just over 120 hours – or 3 weeks – for one person to complete. OS-INT pulls all applicable content into a comprehensive report. The results are prioritized, making it easy to further analyze the findings and determine potential risk. The system allows for continuous monitoring and evaluation, mapping previous results against results from more recent queries.
NET-INT’s massive system of data ingestion has the capability to catalog, index and redeploy internet content related to risk dimensions. The system captures an IP addresses’ pattern of life data, prioritizing anomalous behavior. NET-INT also screens IP addresses associated with an entity or person of interest against all IP addresses displaying anomalous behavior collected over the system’s lifespan. NET-INT’s continuous monitoring of a POI’s Internet research behavior helps predict emergent behavior.
HUM INT’s S4 app can be configured as a workplace tool, allowing veterans and active duty personnel to submit information related to potential suicidal behaviors exhibited by friends or other service members. A centralized management portal allows clients to access real-time threats to geo-fenced facility locations, allowing for timely intervention to support those in need.
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