As a high school student, mental health recovery is often brought up. But what about prevention? I feel that we as a society focus too much on recovery when we should be using our access to technology to find ways to have early intervention for mental health issues. If we are always trying to recover, the problem will continue to persist.
According to the National Institutes of Health, nearly 20% of adolescents and teens ages 3 to 17 have a mental, emotional, developmental, or behavioral disorder. Suicidal behaviors have increased by more than 40% among high school students in the decade before 2019, before the pandemic. Yet only around 19% of these teens and 11% of the adolescents have reported receiving treatment. We need to find a way to detect these issues early before they reach a critical level.
I have been fascinated by the potential that Artificial Intelligence (AI) has in addressing early intervention. With AI, we can detect symptoms of mental health issues in a much earlier stage. If someone is at risk of developing a mental health issue or it seems that they are developing one, we can use AI to identify what mental health issue the person is at risk of and use preventative or intervention measures to ensure that the person doesn’t develop a mental health issue any longer.
We at the Blue SkAI are working to create Machine Learning Models, training and testing with real-world data to develop an accurate detection solution. We are looking for correlation between certain symptoms or telltales with mental health issues. For example, we can use this model to detect early signs of depression in an individual if they feel sad or “empty” often, feel hopeless, pessimistic, or irritable, feel guilty, worthless, or helpless, and if they are losing/have lost interest in hobbies or everyday activities.
Early detection of mental health issues is key because they affect millions of people, but these people may not even realize themselves, and mental health is often stigmatized. AI can provide a method to help these people early on. Traditional methods of diagnosis often include self-diagnosis, which may not provide very accurate results. Using training data sets, we can develop a machine-learning model that finds patterns in symptoms and correlates them to the correct mental health disorder.
In addition to early detection and intervention, AI can help with another key part of helping those suffering from mental health issues. Self-care. Self-care is a vital part of everyday life and should be practiced. Self-care is not talked about as much; it is more about how we can solve mental health issues and find external methods. The biggest remedy and preventative action is self-care. It allows you to take care of yourself, prevent or reduce burnout and stress, and overall, take care of your mental health. Self-care can be as simple as a few minutes of meditation every day, or cooking a meal for yourself. We at the Blue SkAI are working to implement personality quizzes or self-reported test results with AI to generate personalized recommendations for hobbies and interests.
Using AI and large language model chatbots, we can develop personalized self-care plans and even create schedules. Based on an individual’s personality and preferences, certain activities or hobbies can be recommended. Individuals can try pursuing new activities or focus on certain activities that help their mental health more. In this time and age, where we are more isolated than ever before, and behind a screen for multiple hours a day, we need to take action for ourselves, and we can use technology which is so staple to our lives to benefit us.
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