use cases

Predict Suicide Signs

Predicting and preventing suicide, particularly through the use of machine learning algorithms


Prevent suicides and save lives by predicting ahead of time who is likely to commit suicide. Suicide is a major public health concern in the United States, with the CDC reporting that it is the 10th leading cause of death overall, and the second leading cause of death among military veterans. According to the US Department of Veteran Affairs, 16.8 veterans die by suicide every day. While suicide may sometimes seem to come out of nowhere, research has shown that 90% of people who die by suicide had a diagnosable mental health condition that could have been treated (1).

Veterans are particularly at risk for suicide, as they may be struggling with post-traumatic stress disorders from their time in deployment. While there are efforts from government and healthcare institutions to create safe spaces for individuals struggling with suicidal thoughts, these efforts are often reactive, rather than proactive. As a result, many people may not receive the care and support they need until it is too late, as their mental health has deteriorated over a long period of time. It is important that we prioritize proactive, preventative approaches to addressing suicide, so that we can better support individuals at risk and prevent these tragedies from occurring (2).

Size of the Problem

  • 10th leading cause of death overall (3).
  • 2nd leading cause of death among veterans (4).
  • 16.8 veterans die every day due to suicide (5).

Why it matters

The impact of suicide goes beyond the individual who dies by suicide. Suicide can have a profound and lasting impact on the loved ones of the person who dies, as well as on the wider community. This can lead to a range of negative outcomes, such as increased rates of mental health problems, substance abuse, and other health problems among those who have lost a loved one to suicide (2).

Addressing suicide is a complex and multifaceted challenge, requiring a range of interventions and approaches. This can be resource-intensive for hospitals and the healthcare system, as it may require specialized training for healthcare providers, the development of specialized protocols and protocols for care, and the use of advanced technologies and treatments. All of these factors contribute to the importance of suicide as a major public health concern for hospitals and the healthcare system in the US (3).


Artificial intelligence (AI) has the potential to play a role in predicting and preventing suicide, particularly through the use of machine learning algorithms. These algorithms can analyze large datasets of information related to suicide and identify patterns or risk factors that may be indicative of an increased risk of suicide. This information can then be used to develop predictive models that can help hospitals and other healthcare providers identify individuals at risk of suicide, so that they can intervene and provide appropriate support and care.

One example of an AI-based tool that is being used to predict suicide is the VA`s Suicide Data Repository, which uses machine learning algorithms to analyze data from the VA`s electronic health records, as well as other sources of information. The tool is designed to identify individuals at risk of suicide, so that healthcare providers can intervene and provide appropriate support and care.


  • Health Information System (HIS): Collects data on diseases and health events, including suicides, providing relevant demographic and medical information.
  • Mental Health Surveys: Provide data on the prevalence of mental disorders and access to mental health services, important for understanding suicide risk.
  • Public Health Institutions and Research Organizations: May have databases on suicide prevention programs and reported cases.
  • Records from Mental Health Institutions: Contain detailed information on patients with suicidal ideation and suicide attempts.
  • Socioeconomic Databases: Provide information on risk factors related to poverty, unemployment, and access to social services.


  1. Lejeune A, Le Glaz A, Perron PA, Sebti J, Baca-Garcia E, Walter M, Lemey C, Berrouiguet S. Artificial intelligence and suicide prevention: a systematic review. Eur Psychiatry. 2022 Feb 15;65(1):1-22. doi: 10.1192/j.eurpsy.2022.8. Epub ahead of print. PMID: 35166203; PMCID: PMC8988272.
  2. Kumar, V., Sznajder, K.K. & Kumara, S. Machine learning based suicide prediction and development of suicide vulnerability index for US counties. npj Mental Health Res 1, 3 (2022).
  3. Khan NZ, Javed MA. Use of Artificial Intelligence-Based Strategies for Assessing Suicidal Behavior and Mental Illness: A Literature Review. Cureus. 2022 Jul 25;14(7):e27225. doi: 10.7759/cureus.27225. PMID: 36035036; PMCID: PMC9400551.
  4. Parsapoor, M., Koudys, J. W., & Ruocco, A. C. (2023). Suicide risk detection using artificial intelligence: the promise of creating a benchmark dataset for research on the detection of suicide risk. Frontiers in Psychiatry, 14.
  5. Artificial intelligence may improve suicide prevention in the future. (n.d.). UNSW Sites.

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