How AI is helping to predict and prevent suicides

A 2018 pilot project between the Public Health Agency of Canada and Advanced Symbolics will use social media posts as a resource to predict regional suicide rates. (Shutterstock)

How AI is helping to predict and prevent suicides









AI predicts suicide rates


Six-year-old Chenille Tomagatick visits a relative’s grave at a cemetery in the northern Ontario First Nations reserve in Attawapiskat, Ont., on Tuesday, April 19, 2016. The James Bay community struggles with high rates of youth suicide attempts. (THE CANADIAN PRESS/Nathan Denette)



Facebook alerts emergency responders





Chatbots give therapy for depression




Chatbots can deliver psychological interventions for depression and anxiety based on cognitive behavioural therapy (CBT). Since chatbots uniquely respond to presented dialogue, they can tailor interventions to a patient’s emotional state and clinical needs. These models are considered quite user-friendly, and the user-adapted responses of the chatbot itself have been well reviewed.



AI models predict individual risk



When someone puts one leg over the railing of the Golden Gate Bridge and contemplates plunging into the San Francisco Bay, police officers can often persuade them to climb back onto the bridge. Pictured in this 2013 file photo is California Highway Patrol Sergeant Kevin Briggs. (AP Photo/Eric Risberg, File)

AI can also model the combined effect of multiple factors on suicide, and use these models to predict individual risk.

As an example, researchers from Vanderbilt University recently designed an AI model that predicted suicide risk, using electronic health records, with 84 to 92 per cent accuracy within one week of a suicide event and 80 to 86 per cent within two years.

Moving forward with caution

As the field of suicide prevention using artificial intelligence advances, there are several potential barriers to be addressed:

  1. Privacy: Protective legislation will need to expand to include risks associated with AI, specifically the collection, storage, transfer and use of confidential health information.

  2. Accuracy: AI accuracy in correctly determining suicide intent will need to be confirmed, specifically in regards to system biases or errors, before labelling a person as high (versus low) risk.

  3. Safety: It is essential to ensure AI programs can appropriately respond to suicidal users, so as to not worsen their emotional state or accidentally facilitate suicide planning.

  4. Responsibility: Response protocols are needed on how to properly handle high risk cases that are flagged by AI technology, and what to do if AI risk assessments differ from clinical opinion.

  5. Lack of understanding: There is a knowledge gap among key users on how AI technology fits into suicide prevention. More education on the topic is needed to address this.

Overall, AI technology is here to stay in many aspects of health care, including suicide screening and intervention delivery.