Friday July 19, 2019

ADD: Most Common Diagnosis in Child Suicides

Nearly a third of the children aged 5 to 11 who were considered for the study had a known mental health problem.

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A new study suggests that ADD is the most common diagnosis in child suicides

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This article originally appeared on Medical Daily. Suicide is always a hard topic to address, and losing a child to suicide can be especially difficult for a family to comprehend. Although many individuals who choose to take their own lives often suffer from depression, a new study has found that attention deficit disorder (ADD) is actually….repubhubembed{display:none;}


  • Enakshi Roy Chowdhury

    Children are being affected with mental problem named ADD because in today’s world we do not come back home and get our mother waiting for us with our food ready, we do not get to see our dad days after days. This is pushing the children towards loneliness and hence ADD

  • Antara

    We should pay heed to the mental wellbeing of the children of such tender age!

  • Aakash Mandyal

    In this world of Globalization we are moving too fast. We are in a rat race. Children are delicate matter to understand and parents are the one who can counsel them. There is no need to bring children to counsellors if parents shows empathy.

SHARE
  • Enakshi Roy Chowdhury

    Children are being affected with mental problem named ADD because in today’s world we do not come back home and get our mother waiting for us with our food ready, we do not get to see our dad days after days. This is pushing the children towards loneliness and hence ADD

  • Antara

    We should pay heed to the mental wellbeing of the children of such tender age!

  • Aakash Mandyal

    In this world of Globalization we are moving too fast. We are in a rat race. Children are delicate matter to understand and parents are the one who can counsel them. There is no need to bring children to counsellors if parents shows empathy.

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Novel AI Tool to Detect Depression Via Sound of Your Voice

Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time

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Depression is a major issue affecting millions of people, especially the teenagers. Pixabay

India — the sixth most depressed country in the world — has an estimated 56 million people suffering from depression and 38 million from anxiety disorders, according to a recent report by the World Health Organisation (WHO).

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through sound of your voice to gauge whether you are depressed or not.

Computing science researchers from University of Alberta in Canada have improved technology for identifying depression through vocal cues.

The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood.

Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues.

A realistic scenario is to have people use an app that will collect voice samples as they speak naturally.

artificial intelligence, nobel prize
“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time. Much like you have a step counter on your phone, you could have a depression indicator based on your voice as you use the phone,” said Stroulia.

Depression is ranked by WHO as the single largest contributor to global disability. It is also the major contributor to suicide deaths.

The ultimate goal, said researchers, is to develop meaningful applications from this technology.

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Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time.

“This work, developing more accurate detection in standard benchmark data sets, is the first step,” added Stroulia while presenting the paper at the Canadian Conference on Artificial Intelligence recently. (IANS)