The clue is in the title.
Guessed it? The world could be a source of stress and emotional headaches, with some of us able to handle them and others not, or not quite.
For the millions who may not be able to cope for some reason or another and sometimes for no reason at all, well, they fall into a depression.
And while it is a serious problem, those suffering from it often shy away from discussing it, preferring to remain anonymous, or feel they can’t get help on a dedicated basis, any time they needed it.
Here’s your opportunity: An AI Depression App.
The sensitive and personal nature of the content shared in therapy makes anonymous programs using AI very appealing.
Some do already exist: Youper – Emotional Health, Woebot for mood enhancement, Wysa and pacifica, among others.
Why not come up with your own virtual therapist that caters to residents of this region?
Feeling better already?
AI to the rescue
With a preference for convenience and instant feedback, artificial intelligence (AI) is gaining ground with patients in mental and behavioral healthcare.
Many people are not able to access treatment. According to the Substance Abuse and Mental Health Service Administration (SAMHSA)’s 2016 report on drug use and health, only 63% of adults identified as having had at least one major depressive episode reported receiving any kind of treatment.
For teens with major depression, the numbers are even more concerning: Only 40% of teens who had at least one major depressive episode received treatment.
Here are a few ways AI could be used to help people manage depression, as well as what the technology can’t do.
AI apps can provide 24/7 accessibility at little to no cost. According to Verywellmind, the programs collect data that allows them to create a level of therapeutic rapport with users and offer relevant responses.
Machine learning elevates the level of engagement between AI and patient to improve results significantly.
Let’s talk about it
A New AI program was found better at detecting depressive language in social media posts.
The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive.
A Twitter post saying that somebody is depressed because Netflix is down isn’t really expressing depression. Developers are to “explain” this to the algorithm so as not to confuse this with other more symbolic words that do express depressive langauge
According to Folio.co, one example is “Yesterday was difficult … and so is today and tomorrow and the days after,” compared with “Last night was not a good night for sleep … so tired and I have a gig tonight … yawnnn,” which is more an expression of frustration.
Expressive language that includes suicide notes, emotional love letters, drug addictions and family troubles are all part of learning robust language models about depression.
Let’s hear it
A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to a University of Vermont report, based on new research published in the Journal of Biomedical and Health Informatics.
Around 1 in 5 children under the age of 8 suffer from anxiety and depression. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.
Early diagnosis is critical because children respond well to treatment while their brains are still developing, but if they are left untreated they are at greater risk of substance abuse and suicide later in life.
Researchers used an adapted version of a mood induction task called the Trier-Social Stress Task, which is intended to cause feelings of stress and anxiety in the subject. A group of 71 children between the ages of three and eight were asked to improvise a three-minute story, and told that they would be judged based on how interesting it was. The researcher acting as the judge remained stern throughout the speech, and gave only neutral or negative feedback. After 90 seconds, and again with 30 seconds left, a buzzer would sound and the judge would tell them how much time was left.
The researchers used a machine learning algorithm to analyze statistical features of the audio recordings of each kid’s story and relate them to the child’s diagnosis. They found the algorithm was highly successful at diagnosing children, with a diagnosis of a depression disorder with 80% accuracy.
The next step will be to develop a speech analysis algorithm into a universal screening tool for clinical use, perhaps via a smartphone app that could record and analyze results immediately. The voice analysis could also be combined with the motion analysis into a battery of technology-assisted diagnostic tools, to help identify children at risk of anxiety and depression before even their parents suspect that anything is wrong.