A New Artificial Intelligence Identify People With Suicidal Thoughts

The statistics say that nearly 800,000 persons die every year by suicide. And this number will increase because it is very hard to predict their deaths. In the last few years, there were predictions of how will be the artificial intelligence in the future. One of these predictions is the artificial intelligence will enable scientists to identify people with suicidal thoughts. Nowadays, it’s no longer just a prediction by scientists. Thanks to a new study, there will be a machine able to identify the brain activities and save people from killing themselves.2 min


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A New Artificial Intelligence Identify People With Suicidal Thoughts
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The statistics say that nearly 800,000 persons die every year by suicide. And this number will increase because it is very hard to predict their deaths.
In the last few years, there were predictions of how will be the artificial intelligence in the future. One of these predictions is the artificial intelligence will enable scientists to identify people with suicidal thoughts.

The interesting new study

Nowadays, it’s no longer just a prediction by scientists. Thanks to a new study, there will be a machine able to identify the brain activities and save people from killing themselves.

Dr. Marcel, a psychologist from Carnegie Mellon University, explains “Our latest work is unique insofar as it identifies concept alterations that accompany suicidal tendency and behavior,”.

And he continues, “This gives us a window into the brain and mind, shedding light on how suicidal individuals think about suicide and emotion-related concepts.”

The way how they did their study

They tried to know what suicidal tendency looks like in electrical activities of the brain.
Dr. Marcel and his team picked up 34 participants of young adults.
There were about 17 persons attempted suicide previously and have suicidal tendencies, and the other 17 were intact.

They examined the two groups by an FMRI machine to undergo brain imagining while presenting about 30 words.

Ten words were related to suicide (like desperate), the other ten were positive (like hope), and the last words were negative (like trouble).

During the examination, the researchers recorded their emotional responses. After having the results, the scientists found that five words have a marked impact on their brains. These words were: death, trouble, carefree, cruelty, good, and praise.

Then, they analyzed the results using a machine-learning algorithm able to identify suicidal thoughts through measuring the brain activity.

The machine recognized 15 persons of the suicide group (17 patients) and 16 of the healthy groups.

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Lastly, the machine was trained more and recognized persons of suicidal thoughts in 94% of cases. It also distinguished between persons who had previously attempted suicide and others who hadn’t.

“Further testing of this approach in a larger sample will determine its generality and its ability to predict future suicidal behavior, and could give clinicians in the future a way to identify, monitor and perhaps intervene with the altered and often distorted thinking that so often characterizes seriously suicidal individuals,” says David Brent, senior researcher from the University of Pittsburgh.

The doubts concerning the study

This artificial intelligence Aroused great controversy in the whole world. There are many scientists talked about it.

Derek Hill, a medical imaging researcher, said “There are challenges to the routine use of their method in a healthcare setting. The sort of  brain imagining that the researchers employed is only available in advanced research institutions, and also requires cooperative patients, so wouldn’t be widely available to mental health patients soon.”

Additionally, Blake Richards, explained, “There is undoubtedly a biological basis for whether someone is going to commit suicide,”. He is a neuroscientist at the University of Toronto in Canada.

And he continues, “ But the question is whether the biological basis for these things are sufficiently accessible by FMRI to develop a scientific test that they can use in a clinical setting.”

References

Marcel Adam Just, Lisa Pan, Vladimir L. Cherkassky, Dana L. McMakin, Christine Cha, Matthew K. Nock & David Brent, “Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth”.

 

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