Teenage suicide is really difficult to predict: Machines could fill that gap
The best apps will have predictive machine learning algorithms
In any given week, Ben Crotte, a behavioural health therapist at Children’s Home of Cincinnati in the US, speaks to dozens of students in need of an outlet. Their challenges run the adolescent gamut, from minor stress about an upcoming test to severe depression, social isolation and bullying.
Amid the flood of conversations, meetings and paperwork, the challenge for Crotte — and mental health professionals everywhere — is separating hopeless expressions of pain and suffering from crucial warning signs that suggest a student is at risk for committing suicide.
It’s a daunting, high-pressure task, which explains why Crotte was willing to add another potentially useful tool to his diagnostic kit: An app that uses an algorithm to analyse speech and determine whether someone is likely to take their own life. Its name: “Spreading Activation Mobile” or “SAM.”
“Losing a child is my worst nightmare, and we all live with the fear that we might miss something,” Crotte said, referring to mental health professionals who work in education. “Sometimes we have to go with our gut to make a decision, so this is one more tool to help me make a final determination about someone’s health.”
SAM is being tested in a handful of Cincinnati schools this year and arrives at a time when researchers across the country are developing new forms of artificial intelligence that may forever change the way mental health issues are diagnosed and treated.
“Technology is here to stay, and if we can use it to prevent suicide, we should do that,” said physician Jill Harkavy-friedman, vice president of research at the American Foundation for Suicide Prevention.
There are thousands of apps dedicated to improving mental health, but experts say the most promising will begin to incorporate predictive machine learning algorithms into their design. By analysing a patient’s language, emotional state and social media footprint, these algorithms will be able to assemble increasingly accurate, predictive portraits of patients using data that is far beyond the reach of even the most experienced clinicians.
“A machine will find 100 other pieces of data that your phone has access to that you wouldn’t be able to measure as a psychiatrist or general practitioner,” said Chris Danforth, a University of Vermont researcher who helped develop an algorithm that can spot signs of depression by analysing social media posts.
Using data from more than 5,000 adult patients with a potential for self-harm, Colin Walsh, a data scientist at Vanderbilt University Medical Centre, also created machine-learning algorithms that predict the likelihood that someone will attempt suicide within the next week. The risk detection is based on such information as the patient’s age, gender, medications and prior diagnoses.
Danforth’s algorithm — which he developed with Harvard researcher Andrew Reece — can spot signs of depression by analysing the tone of a patient’s Instagram feed. The pair created a second algorithm that pinpoints the rise and fall of someone’s mental illness by scanning the language, word count, speech patterns and degree of activity on their Twitter feed.
“The dominant contributor to the difference between depressed and healthy classes was an increase in usage of negative words by the depressed class, including ‘don’t,’ ‘no,’ ‘not,’ ‘murder,’ ‘death,’ ‘never’ and ‘sad,’” the researchers wrote in their latest study identifying mental illness on Twitter. “The second largest contributor was a decrease in positive language by the depressed class, relative to the healthy class, including fewer appearances of ‘photo,’
Technology is here to stay, and if we can use it to prevent suicide, we should do that.” Jill Harkavyfriedman, American Foundation for Suicide Prevention
Researchers are developing new forms of artificial intelligence that may forever change the way mental health issues are diagnosed and treated.