Machine Learning to Help Detect Suicide Risks
Brian Resnick wrote about suicide prevention for Vox.com after the recent deaths of two famous people: fashion designer Kate Spade and chef, author and TV star Anthony Bourdain. The story outlines how an AI-based program might help predict who is at the greatest risk of a suicide attempt.
Traumatic events like the deaths of Bourdain and Spade, as well as the 2015 Paris attacks trigger a deluge of calls to suicide hotlines from people in despair. Deciding whom to help first can be a life-or-death decision. At the Crisis Text Line, a text messaging-based crisis counseling hotline, these events can overwhelm the staff. So data scientists at Crisis Text Line are using machine learning, a type of artificial intelligence, to find words and emojis that can signal a person at higher risk of suicide ideation or self-harm.
The computer tells them who on hold needs to jump to the front of the line to be helped. They can do this because Crisis Text Line collects a massive amount of data on the 30 million texts it has exchanged with users. While Netflix and Amazon are collecting data on tastes and shopping habits, the Crisis Text Line is collecting data on despair.
“We do not yet possess a single test, or panel of tests that accurately identifies the emergence of a suicide crisis,” a 2012 article in Psychotherapy explains. And that’s still true.
Doctors understand the risks for suicide ideation better than they understand the risk for physical self-harm. Complicating matters, the CDC finds 54 percent of suicides involve persons with no known mental health issues. But we can do better. And that’s where data scientists like Filbin think they can help fill in the gaps, by searching through reams of data to determine who is at greatest risk and when to intervene. Even small insights help. For example, the crisis text line finds when a person mentions a household drug, like “Advil,” it’s more predictive of risk than if they used a word like “cut.”
The amount of data that is being collected and the way in which that data is interpreted is critical in making this type of prevention work. The article is a comprehensive look at using AI to save lives at risk.
read more at vox.com