Harnessing the Power of AI: Revolutionizing Suicide Prevention for Teens and Veterans
Authors: Amina Khalpey, PhD, Brynne Rozell, DO, Zain Khalpey, MD, PhD, FACS
Artificial intelligence (AI) has become a significant player in the healthcare industry in recent years. The technology is being leveraged in several areas of healthcare, including diagnosis, treatment, and care management. Suicide is a global public health concern that affects individuals of all ages. In the United States, suicide is the second leading cause of death among teenagers and veterans. Suicide risk factors include mental health conditions, substance abuse, stress, trauma, and access to lethal means. The use of AI in healthcare has the potential to identify individuals at risk of suicide and improve access to timely and appropriate interventions. This blog post explores several AI healthcare strategies that can help reduce suicide risk in teenagers and veterans.
The Burden of Suicide Among Teenagers and Veterans
Suicide is a significant public health problem in the United States, affecting individuals of all ages. Sadly, suicide is now a leading cause of death among teenagers and veterans in the United States. Teen suicide is the second leading cause of death among individuals aged 10-24 years. In 2019, there were 6,213 suicides among individuals aged 10-24 years, which translates to an age-adjusted rate of 9.7 per 100,000 individuals (CDC, 2021). Suicide rates among teenagers have been on the rise in recent years. Between 2007 and 2019, the suicide rate among individuals aged 10-24 years increased by 57.4% (CDC, 2021).
According to the Department of Veterans Affairs (VA), an average of 17 veterans die by suicide every day. In 2019, there were 6,435 veteran suicides, which accounted for 7.6% of all suicides in the United States (VA, 2021). Veterans have made immense sacrifices to protect and serve their countries. They often face unique challenges and experiences during their service, including combat exposure, traumatic events, and the transition back to civilian life. These factors can contribute to higher rates of mental health issues and an increased risk of suicide among veterans.
AI Healthcare Strategies to Reduce Suicide Risk in Teenagers and Veterans
AI has the potential to identify individuals at risk of suicide and provide timely and appropriate interventions. The following AI strategies which are outlined below can help reduce suicide risk in teenagers and veterans.
Natural Language Processing (NLP)
Natural language processing (NLP) is a subfield of AI that focuses on the interaction between computers and human language. NLP can be leveraged in suicide prevention by analyzing written and spoken language online for suicide risk factors. Studies have shown that individuals who are at risk of suicide often express suicidal thoughts and feelings in their language use. For example, teenagers who are at risk of suicide may express feelings of hopelessness, worthlessness, and isolation (1). Veterans who are at risk of suicide may express feelings of guilt, shame, and anger.
NLP algorithms can analyze language use in social media posts, chat logs, and other written and spoken communication to identify individuals at risk of suicide. The algorithms can identify keywords and phrases that are associated with suicide risk factors and alert healthcare professionals. For example, a study by Metzler et al. in 2022 found that an NLP algorithm could accurately identify suicidal ideation in social media posts with an accuracy of 88%(2). The use of NLP in suicide prevention can improve early detection and intervention for individuals at risk of suicide.
Predictive modeling is another AI healthcare strategy that can help reduce suicide risk in teenagers and veterans. Predictive modeling uses machine learning algorithms to analyze large datasets and predict outcomes. In suicide prevention, predictive modeling can be used to identify individuals at risk of suicide and provide targeted interventions. Predictive modeling algorithms can analyze electronic health records (EHRs), social media posts, and other data sources to identify risk factors for suicide. The algorithms can then predict which individuals are at highest risk of suicide and alert healthcare professionals.
For example, a study by Walsh et al. (2020) used a machine learning algorithm to analyze EHRs of veterans and predict suicide risk. The algorithm identified 10 risk factors for suicide, including a history of suicide attempts, substance abuse, and psychiatric hospitalizations(3). The algorithm predicted suicide risk with an accuracy of 88.3%. The use of predictive modeling in suicide prevention can improve the accuracy of suicide risk assessments and allow for targeted interventions.
Chatbots are computer programs that use NLP algorithms to simulate human conversation. Chatbots can be used in suicide prevention by providing support and resources to individuals at risk of suicide. Chatbots can be programmed to recognize suicidal language use and provide appropriate interventions. For example, a chatbot could ask individuals about their current emotional state and provide resources for coping with suicidal thoughts and feelings.
Chatbots can be used to provide 24/7 support for individuals at risk of suicide. This is particularly important for teenagers and veterans, who may be reluctant to seek help from healthcare professionals. Chatbots can provide a low-pressure way for individuals to seek help and receive support.
Virtual Reality (VR) Therapy
Virtual reality (VR) therapy is a form of therapy that uses VR technology to simulate real-life situations. VR therapy can be used in suicide prevention as a novel therapy by providing exposure therapy for individuals at risk of suicide. VR therapy provides a controlled and safe environment for individuals to confront and address their emotional challenges. Therapists can design virtual scenarios that gradually expose individuals to situations that trigger their symptoms, allowing them to learn and practice coping strategies in a supportive setting. This controlled exposure can help reduce anxiety and build resilience. VR therapy can simulate these situations in a safe and controlled environment with professionals.
For example, VR therapy can simulate situations that trigger feelings of hopelessness and isolation, such as social rejection or job loss. The therapy can provide coping skills and strategies for managing these feelings. VR therapy can also be used to simulate positive situations, such as social support and healthy relationships (6).
Wearable devices, such as smartwatches and fitness trackers, can also be used in suicide prevention by monitoring physiological and behavioral data. Wearable devices can collect data on heart rate, sleep patterns, and activity levels, among other measures. Changes in these measures can indicate changes in mental health and well-being, alerting healthcare providers to alarming patterns. For example, a study by the University of Utah used a smartwatch to monitor heart rate variability in veterans with PTSD (7). The study found that veterans who had attempted suicide had lower heart rate variability than those who had not attempted suicide. Wearable devices can provide early warning signs of suicide risk and alert healthcare professionals.
Challenges and Limitations of AI Healthcare Strategies in Suicide Prevention
While AI healthcare strategies show promise in reducing suicide risk in teenagers and veterans, there are also challenges and limitations to their use. Some of these challenges include:
The use of AI in healthcare raises privacy concerns. The collection and analysis of personal data for suicide prevention must be done in a way that protects individuals’ privacy and security.
The accuracy of AI algorithms in suicide prevention relies on the quality and quantity of data available. The successful use of AI in suicide prevention requires access to large datasets of high-quality data.
AI algorithms can be biased if they are trained on biased data. The use of AI in suicide prevention must be implemented in a way that avoids bias and ensures fairness.
The use of AI in suicide prevention raises ethical concerns, including the use of personal data, the potential for harm from inappropriate interventions, and the impact on the therapeutic relationship between patients and healthcare professionals.
The implementation of AI healthcare strategies in suicide prevention faces challenges, such as integrating AI algorithms into existing healthcare systems, ensuring accessibility and affordability of AI technology, and training healthcare professionals in the use of AI.
Suicide is a significant public health problem that affects individuals of all ages, but particularly teenagers and veterans. The use of AI in healthcare has the potential to improve suicide prevention by identifying individuals at risk of suicide and providing timely and appropriate interventions. AI healthcare strategies, such as NLP, predictive modeling, chatbots, VR therapy, and wearable devices, can help reduce suicide risk in teenagers and veterans. However, the use of AI in suicide prevention raises challenges and limitations, including privacy concerns, data quality, bias, ethical concerns, and implementation challenges. These challenges must be addressed to ensure the safe and effective use of AI in suicide prevention.
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2. Metzler H, Baginski H, Niederkrotenthaler T, Garcia D. Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach. J Med Internet Res. 2022;24(8):e34705. Published 2022 Aug 17. doi:10.2196/34705
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