According to Joe Franklin, computers are far better than people when it comes to guessing who’s at risk
The U.S suicide rate is at a 30-year high. According to the National Center for Health Statistics, in 2014 (the last year for which figures are available), 42,773 Americans took their own lives, most of them men.
It’s a crisis, one mental health professionals have historically been ill-equipped to handle. Last year, Joseph Franklin (then a postdoctoral fellow at Harvard, now an assistant professor of clinical psychology at Florida State University) looked at 365 studies on suicide over the past 50 years and found that someone flipping a coin had the same chance of correctly predicting whether a patient would die by suicide as an experienced psychiatrist — 50/50.
If humans are so mediocre when it comes to gauging suicidal intentions, could machines be better? Signs point to yes. IBM’s Watson supercomputer diagnosed a rare cancer doctors missed, while in England, the National Health Service is trying out Google’s DeepMind artificial intelligence for everything from diagnosing eye illnesses to finding out how best to target radiotherapy.
The link between A.I and mental health is less hyped, but Franklin and his team have developed algorithms that can predict whether someone will die by suicide with over 80 percent accuracy. He hopes they may soon become standard, in the form of software that every clinician has access to — and thus help save lives.
What made you want to study suicide prediction?
When I got into suicide research, I wanted to look at everything and see where we were. My hope was that would provide me and my colleagues with some more specific direction on what we knew and could build on. And what we found was quite surprising. We figured out that people have been doing this research where we’ve been very bad at predicting suicidal thoughts and behaviors and we really haven’t improved across 50 years.
Are there common misconceptions about suicide risk?
A lot of people believe that only someone who is showing clear signs of depression is likely to have this happen. I’m not saying depression has nothing to do with it, but it’s not synonymous with that. We can conservatively say 96 percent of people who’ve had severe depression aren’t going to die by suicide.
Most of our theories which say this one thing causes suicide or this combination of three or four things causes suicide — it looks like none of those are going to be adequate. They may all be partially correct but maybe only account for 5 percent of what happens. Our theories have to take into account the fact that hundreds if not thousands of things contribute to suicidal thoughts and behaviors.
More men take their lives than women, but more women attempt suicide. Are there any theories why?
One thing people point to now is something called suicide capability, which is basically a fearlessness about death and an ability to enact death, and one assumption is that men, particularly older men, may be more capable of engaging with these behaviors. But evidence on that right now is not conclusive.
Are traditional risk assessments getting some things right?
Talking to people, not making it this taboo subject, I think that’s great. The problem is we haven’t given them much to go on. Our implicit goal has often been to do research so we can tell clinicians what the most important factors are, and what we’re finding is that we’re just not very accurate.
What we’re going to have to do is this artificial intelligence approach so that all clinicians are able to have something that automatically delivers a very accurate score of where this person is in terms of risk. I think we should be trying to develop that instead of, you know, “these are the five questions to ask.”
How does artificial intelligence predict who is most at risk?
We took thousands of people in this medical database and pored through their records, labeled the ones who had clearly attempted suicide on a particular date and ones that could not to be determined to have attempted suicide, and we then let a machine-learning program run its course. We then applied it to a new set of data to make sure that it worked. The machine has now learned, at least within this particular database of millions of people, what the optimal algorithm seems to be for separating people who are and are not going to attempt suicide…