Table of Contents

Depression



Early Detection



Flags



Chatbots to the rescue?

Depression

Kids have panic attacks and corporations tweet self-deprecating jokes. It is no wonder that the United States has seen a shocking increase in depression and suicide rates among young people over the last 20 years. I would personally like to point my finger at the terrifying threat of global warming, college prep preschools, a manifestation of the extremely competitive nature of college admissions, and negligent food scientists who are not even attempting to invent calorie-free french fries. People may not be able to solve some of these problems using technology, but large strides in machine learning and data science can change the way that young people react to and deal with the future. Machine learning technology may not be able to spit out a recipe for delicious, cancer-less, and calorie-free chemicals, but it can help with the early detection and treatment of depression.

Early Detection

Early detection of depression can completely change the course of the disease and how a person manages it. As children grow and learn to navigate the world their personalities are simultaneously developing, so parents and teachers may believe that any signs of depression are just the personality of the child. Unfortunately, parents and teachers, especially to young children, may find it difficult to notice early signs. Some may not even know to look. In the past ten years, however, we have seen suicides in children as young as ten.

Depression screenings during check-ups or in school can prevent these tragedies from happening. AI technology has advanced enough to detect depression in young children through speech patterns recognition. These systems can interpret if a child is speaking too slowly or taking pauses too often, determining whether or not the child’s speech pattern parallels how other children speak when depression is present. This type of technology is not entirely accurate, and cannot diagnose children. However, assisted by audio processing technology, teachers, parents, and caregivers can catch signs and symptoms of depression in young children before they become too serious. If an AI system can flag a child, it is much more likely that the parent can then take the child to a social worker.

Other forms of detection that we may see in the future, but may not be advanced enough to use include:

Natural language processing technology to interpret and flag social media posts

Computer vision technology to recognize depression in facial expressions

facial expressions Image processing technology to detect depression from the color scheme of Instagram posts

We have a long way to go before computers can accurately diagnose depression, but the technology has already advanced enough to effectively begin treatment, or flag young children so that behavioral therapy can begin sooner rather than later.

Flags

However, flagging potential depression is not the only way that AI can help alleviate our mental health crisis. In fact, the first therapy robot, ELIZA, appeared in the 1960’s as a simple tool with the ability to recognize certain words and phrases and respond to them. At the time, many people thought it was a real person on the other side, however, this was only when they talked exclusively about themselves. As the technology advances, people are more likely to use these systems to learn more coping mechanisms. Not only can chatbots feel like a real person to talk to, but these systems can recommend certain ways to move forward. After explaining their symptoms to a trained model, the system can then generate and recommend different tools to help the user re-frame their mindset.

These types of systems seem fairly human and “intelligent”, so people may fear them. However, often people are more scared of seeking a person to speak to. Also, because these deep learning models are able to learn patterns well enough to respond appropriately to people, they can help people find the strength to meet somebody in-person, or flag when someone is a threat to themselves or others.

These types of bots are cheaper to train than sending a person to years of school that therapists must attend. Therefore, it may be an effective way to deliver some form of therapy to crisis-ridden areas. Using a single platform such as Skyl, and a few people with the necessary training to pick out cues of mental illness, creating a chatbot is a fairly straightforward process that doesn’t require an individual to write a single line of code. This means that any company or person with the correct mindset can help thousands of people simultaneously.

This type of platform is not limited to helping depressed people seek help or helping people without access to therapists discover new coping mechanisms. People with high levels of responsibility and little time have fairly high chances of developing depressive symptoms. This is due to the high-stress nature of their jobs, especially in bigger businesses. However, such people are less likely to go to therapists or seek help as they are figureheads of their companies and they can not be allowed to show weakness to their employees. They also have packed schedules, which further reduces the likelihood of going and seeking help. However, chatbots are discreet and do not have the same time commitment going to regular therapy does. The convenience and .

Chatbots to the rescue?

Chatbots tend to glitch when a user gives them an unexpected reply that they do not know how to respond to, generally statements that are not about the user themselves. However, using a platform such as Skyl, which uses deep learning models to learn more as more data is input, can seem very “human”. Many people can use some form of therapy, whether it be learning dialectic or cognitive behavioral therapy, or just speaking about their issues. For pointing to new coping mechanisms, or reminding a user to breathe, these natural language processing models are useful, and could one day be widely used as a way to take care of ourselves. We believe in a future where weekly check-ins with your bot could be as common as taking your vitamins, drinking 64 ounces of water a day, and exercising for 30 minutes. Using a platform such as Skyl, a person who aims to create a natural language processing project could streamline the end-to-end workflow, and ultimately focus on creating a user experience that is catered toward their audience, whether it be young children who do not know how to process feelings or C-Level executives who don’t have an hour a week to spare.

Visit Skyl.ai to learn more about how to create a natural language processing project or computer vision project. Our goal is to make the world a better place through machine learning!