More than 6,000 veterans committed suicide in 2017 – an average of 17 suicides a day.
Veteran Suicide Rate is 1.5 Times the Rate of Non-Veterans
That number was recently reported in the 2019 National Veteran Suicide Prevention Annual Report, along with these equally sobering statistics:
- The veteran suicide rate is 1.5 times the rate for non-veterans.
- Veterans ages 18-34 had the highest suicide rate (44.5 per 100,000). Overall, the suicide rate for this age group has increased by 76 percent since 2005.
- In addition to the veteran suicides, there were 919 suicides among never federally activated National Guard and Reserve members, an average of 2.5 per day.
The report reinforces the magnitude of the crisis facing former members of our military. And, it comes just months after a renewed call for a comprehensive approach to address this national tragedy.
PREVENTS: A Comprehensive National Approach
In March 2019, the Trump Administration announced its FY 2020 budget proposal of $9.4 billion for veteran mental health services, including $222 million for suicide-prevention outreach, a $15.6 million increase over 2019.
That same month, President Trump also issued an Executive Order on a National Roadmap to Empower Veterans and End Suicide (PREVENTS). The PREVENTS Initiative calls for the development of a comprehensive public health strategy across all levels of government, and the private and non-profit sectors.
The goal is to understand the underlying factors of suicide, cultivate active engagement with veterans, and increase the timely identification of risk and intervention for those in need.
Increasing Timely Identification and Intervention
A national research strategy is among the key components of PREVENTS. The Office of Science and Technology Policy (OSTP) is tasked with leading efforts to improve the coordination, monitoring, benchmarking, and execution of suicide-related data and research.
In its Request for Information, the OSTP announced its milestones and metrics would be focused on improving the ability to identify individual veterans and groups of veterans at greater risk of suicide and draw upon technology to capture and use health data from non-clinical settings to help target prevention and intervention strategies.
AI as an Early Detection System
Some experts believe that machine learning can be part of the solution when it comes to early intervention and risk prediction, suggesting that AI can be an early detection system by identifying and monitoring behaviors indicative of suicidal ideation.
One study they point to, conducted by researchers at the New York University School of Medicine, and funded by a grant from the U.S. Army Medical Research and Acquisition Activity, used speech-based algorithms to help detect post traumatic stress disorders (PTSD) from warzone-exposed veterans.
Speech-based Algorithms Help Identify PTSD
The study analyzed audio recordings of clinical interviews, creating 40,526 speech features that were input into an algorithm, and ultimately shaved down to eighteen specific markers indicative of the potential for PTSD. The algorithm correctly classified cases 89.1% of the time based on slower, more monotonous speech characteristics, less change in tonality, and less variation in activation.
Machine learning and AI are also being used to better analyze the Veterans Administration’s (VA) electronic health records to identify key factors related to suicide risks.
Deep Learning Neural Networks Predict Risk Based on Physicians’ Notes
A collaboration between the VA, the Department of Energy (DOE) and researchers at Lawrence Berkeley National Lab, focused on building deep learning neural networks that could distinguish between patients at high risk and those who are not based on physicians’ and discharge notes.
Among the challenges was the noisy data sets that included structured data such as lab work and procedures and the unstructured data, like handwritten notes. But, as one researcher on the project pointed out, the value is in that unstructured data:
“We believe that, for suicide prevention, the unstructured data will give us another side of the story that is extremely important for predicting risk — things like what the person is feeling, social isolation, homelessness, lack of sleep, pain, and incarceration. This kind of data is more complicated and heterogeneous, and we plan to apply what we have learned …to help VA doctors better decide who is at high risk and who they need to reach out to.”
The Path Ahead
The PREVENT Task Force’s mandate is to submit a proposed roadmap forward by next March. The possibilities presented by AI and machine learning suggest that this technology should be a key area of focus, with continued investment and research.
While early identification of suicidal behaviors and risk is just one piece of helping end this national tragedy, it is a critical component of the overall strategy – and AI can play an important role.
To contact the Veteran Crisis Line, callers can dial 1-800-273-8255 and select option 1 for a VA staffer. Veterans, troops, or their family members can also text 838255 or visit VeteransCrisisLine.net for assistance.
The National Suicide Prevention Lifeline is 1-800-273-8255.