It is no secret that human beings are in a mental health crisis.
Whether you attribute this to the social media syndrome, or the social balance between the “purpose and power†mentioned by Yuval Hurley in Homo Deus, in fact, the two say It's all one thing. But this is prone to frequent depression, longer treatment periods, and the most disturbing suicidal tendencies.
Sadly, our ability to diagnose mental illness at this stage lags far behind the diagnosis of physical illness.
Today, thanks in large part to the revolution in artificial intelligence and bioinformatics, researchers are working hard to unlock many of the mysteries behind mental illness. It is worth mentioning that a new paper by scientists from the University of Southern California, Carnegie Mellon University and the Cincinnati Children's Hospital Medical Center claims to have a certain biomarker for distinguishing between depression and suicide. the study.
You may think that you are good at interpreting other people's expressions and emotions. However, if you want to distinguish whether a person wants to commit suicide or only has a tendency to depression, these differences are very subtle.
To clarify these differences, the researchers looked at facial expressions from three groups of people who wanted to commit suicide, people with depression, and people in the medical control group. In the survey of the three groups, they recorded a series of information including smiles, frowns, eyebrows and head movements. These data are then entered into a machine learning algorithm to find the association between the different actions.
It is worth noting that frowning is not the most obvious feature between a depressed person and a suicidal patient. A smile is. Specifically, the psychological Duchenne smile and the non-Ducheni smile are the key to distinguishing between these two groups. Ducheni smiles include the contraction of the muscles around the eyes, while for non-Ducheni smiles, the eyes don't laugh. Those who show a non-Ducheni smile are more likely to have suicidal thoughts than those who have no smile at all.
Researcher Dr. Morency revealed, "I hope this algorithm can help doctors distinguish patients with depression or suicide in clinical treatment." Obviously, patients with suicidal tendencies need to observe differently from patients with mild depression. However, research like Dr. Morency often raises many questions. In particular, what happens when an algorithm knows more about humans than humans themselves? From the speed of Facebook and Google's application of data mining methods to social media accounts, this day may not be far off. Unfortunately, people like Dr. Morency are not able to predict the unintended consequences of these studies in the future.
Scientists often draw a line between breakthroughs in research and social applications. For example, an algorithm like Dr. Morency, if used by an insurance company rather than a clinician to treat a disease, can be unpleasant. Suicidal tendencies can have a negative impact on purchasing life insurance, job promotion, and even job hunting. Unless we begin to delve into the application of such algorithms and urge legislators to set reasonable standards for their regulation, machine learning is likely to become a demon.
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