AI Case Study

Researcher at the Royal Ottawa Mental Health Centre identifies possible signs of suicidal thoughts in public posts using machine learning

Zachary Kaminsky, a suicide researcher at Royal Ottawa Mental Health Centre analysed four years of public tweets by Anthony Bourdain, following his suicide. In a trial of his algorithm, he tried to sort tweets according to hopelessness, loneliness and other characteristics that may be possible signs of distress and suicidal thoughts. The system was able to find patterns in the data including a total of 250 days that the chef could have been experiencing significant mood swings as well as an increase in potentially alarming behaviour through his social media account during the last 20 days before his death. the ultimate purpose of this research is to examine whether AI can be of assistance in predicting suicide risk in time to intervene.

Industry

Technology

Software And It Services

Project Overview

"A few days after the death of Anthony Bourdain last summer, a suicide researcher at the Royal Mental Health Centre in Ottawa downloaded four years of public tweets by @Bourdain onto the computer in his office.

Zachary Kaminsky was certainly not the only one parsing the stream of Mr. Bourdain’s personal tweets for clues to why the the famed chef, author and television travel personality had taken his own life. But he had a unique tool – a computer algorithm designed to identify and sort tweets by characteristics such as hopelessness or loneliness to reveal a pattern only a machine could find.

In simple terms, the algorithm, still in the early trial stage, charts the mood of the person tweeting in real time, with the goal of using that information to peer into the future. The idea being tested by Dr. Kaminsky, and researchers around the world, is whether artificial intelligence can predict suicide risk in time to intervene.

The line graph that Dr. Kaminsky’s computer created for Anthony Bourdain tells a clear story. Over 250 days, according to the algorithm, his mood goes up and down, spiking in a couple spots over the baseline score for suicidal thoughts – what experts call “ideation” – but always dropping out of danger a few days later. Then, around 20 days before his death, the line begins to rise steeply, and it keeps rising, as if climbing a skyscraper, until the fateful day of his suicide. In those final weeks of tweets, Mr. Bourdain defends the women making charges against Harvey Weinstein, mocks the royal wedding and grumbles about criticism regarding a Newfoundland segment on his show. There’s nothing particularly remarkable. But according to Dr. Kaminsky’s algorithm, the machine found tragedy brewing.

This doesn’t mean the algorithm works every time – these are preliminary tests, Dr. Kaminsky says. The AI program uses a machine-learning approach designed to improve as it’s trained with more examples. It recognizes subtle patterns in words related to mood – not only phrases such as “I feel lonely,” but also “my friends are ignoring me.” The more phrases, the better it works.

Reported Results

The algorithm revealed potentially significant mood swings over 250 days and a exponential rise above the baseline score for suicidal thoughts 20 days before his death.

Technology

Function

Background

"Suicide is a rare event – in Canada, the annual rate is 11 deaths per 100,000 people. Still, every day, 10 Canadians die by suicide, and in recent years those numbers appear to be slightly rising. In many cases, there are signs of trouble brewing. The majority of victims suffer from a mental illness. They have usually sought help from a doctor or visited an emergency room. Many have families trying desperately to get the right help. They exist in the system, as data points, medical results, appointments and paperwork. Marshal all that information with a clever algorithm and epigeneticists such as Dr. Kaminsky propose that lives might be saved."

Benefits

Data

Public data such as tweets