Mental health refers to the state of our mind, including how we think, feel, and react when something changes in the environment or community. It also includes how it affects our daily lives, how we handle stress, and any kind of situation. According to the WHO, 2443 people in India per 10,000 population face mental health issues per year. This should be a cause for concern. As everyone's experience with depression is different, patients have to go through medication after medication before finding a final solution. This is not only a lengthy process but also leads to self-harm and even loss of life. However, people are becoming more concerned about this, leading to the use of machine learning (ML) and artificial intelligence (AI).

Research by MIT's Rosalind Picard and Massachusetts General Hospital's Paola Pedrelli shows that AI can help reduce the rate of mental disorders. The main issue with mental health is that it cannot be identified before it's too late or at the beginning. However, a machine can be very good at noticing and identifying particular human patterns of behavior, activities, socialization, and sleep patterns to identify if a person is in depression or going to face any mental health issues. This is the current work of Picard & Pedrelli. If machine learning could use its vast amount of data to identify a person's current emotional and mental state and predict problems and stages, it could be present in the form of physical or virtual assistance using AI to work with the condition. There are several ways AI can be used to fight against depression and anxiety, such as:

• Using data to find the root cause: AI and ML can hold a significant amount of biological data, such as MRIs, electroencephalography, pat scans, electrocardiography, and genetic markers, to gain a better understanding of what is happening in a person's brain and mind. Recent studies show this is actually possible to identify and start treating.
• Identifying patterns to treat: AI can identify specific patterns of emotional conditions of a person and find what type of depression they are going through. As we know, depression and mental health vary from person to person and behave differently for each one. It's necessary to know the specific pattern of the brain of that particular patient.
• Starting treatment at an early stage: Whenever a doctor identifies the root cause and patterns of a patient's emotional state, it will be easy to start the treatment. Using machine learning and artificial intelligence to group distinct patients with different responses and biological causes can help doctors treat them at an early stage.

Medication and some common symptoms may not always be treated correctly and effectively. AI could be used as a network and middleman between the patient and the doctor itself. It will record the patient's language and translate that language into a barcode with different colors of dialogue capture moment by moment, which a doctor can analyze and start treatment with a better understanding of the person's thoughts.

Conclusion

Mental health is a significant field that needs attention. Studies show that an estimated 800,000 people die by suicide worldwide each year. In India alone, about 164,033 Indians committed suicide in 2021 due to different severe depressive causes. If it's possible to use the facilities offered by AI and ML, then why not use them? They can identify data, patterns, and help doctors and psychologists with incredible accuracy. They are dealing with real-life problems in the real world. A machine can never replace itself with human brains or real-world people, but it could be used to classify accurate biological and emotional patterns happening in the patient's brain at a very early stage to treat mental health problems and even prevent severe depression and anxiety. Most importantly, it could save lives.

https://news.mit.edu/2022/deploying-machine-learning-improve-mental-health-rosalind-picard-0126