Can a Smartphone Predict a Suicide before it happens? Maybe!
There are few areas In the field of mental health generating as much excitement as machine learning, which uses computer algorithms to better predict human behavior. There is an exploding interest in biosensors that track a person’s mood in real time, factoring in music choices, social media posts, facial expression and vocal expression.
Matthew K. Nock, a Harvard psychologist who is one of the nation’s top suicide researchers, hopes to combine these technologies together into an early-warning system that could be used when an at-risk patient is released from the hospital.
In the Times article he offers this example of how it could work:
There are reasons to doubt that an algorithm can ever achieve this level of accuracy. Suicide is such a rare event, even among those at highest risk, that any effort to predict it is bound to result in false positives, forcing interventions on people who may not need them. False negatives could thrust legal responsibility onto clinicians.
Algorithms require long-term data from a large number of people, and it’s nearly impossible to observe large numbers of people who die by suicide. Finally, the data needed for this kind of monitoring raises red flags about invading the privacy of some of society’s most vulnerable people.
Dr. Nock is familiar with all these arguments but has persisted, in part out of sheer frustration.
“With all due respect to people who’ve been doing this work for decades, for a century, we haven’t learned a great deal about how to identify people at risk and how to intervene,
The suicide rate now is the same it was literally 100 years ago. So just if we’re being honest, we’re not getting better.”Matthew K. Nock
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