Feeling Blue? Machine Learning is Here to Predict your Mental Health

Machine Learning

Machine LearningHow can ML be used in the detection, diagnosis, and treatment of mental health problems?

The increase of mental health problems and the need for effective medical health care have led to an investigation of machine learning that can be applied to mental health problems. The high prevalence of mental illness and the need for effective mental health care, combined with recent advances in AI, has led to an increase in explorations of how the field of machine learning (ML) can assist in the detection, diagnosis, and treatment of mental health problems. On that note, this article features the 

ML techniques can potentially offer new routes for learning patterns of human behavior; identifying mental health symptoms and risk factors; developing predictions about disease progression, and personalizing and optimizing therapies. Despite the potential opportunities for using ML within mental health, this is an emerging research area, and the development of effective ML-enabled applications that are implementable in practice is bound up with an array of complex, interwoven challenges. For years, the industry has seen tools like chatbots and virtual assistants as a viable way of wading into the waters of AI. With the onset of COVID-19 – and all the stressors that came with it – organizations have turned to AI to potentially broaden access to and availability of mental health services. 

Machine learning is a type of AI technology where, when the machine is given lots of data and examples of good behavior (i.e., what output to produce when it sees a particular input), it can get quite good at autonomously performing a task. It can also help identify meaningful patterns, which humans may not have been able to find as quickly without the machine’s help. Using wearable devices and smartphones of study participants, researchers can gather detailed data on participants’ skin conductance and temperature, heart rate, activity levels, socialization, personal assessment of depression, sleep patterns, and more. Their goal is to develop machine learning algorithms that can intake this tremendous amount of data, and make it meaningful to identify when an individual may be struggling and what might be helpful to them. They hope that their algorithms will eventually equip physicians and patients with useful information about individual disease trajectories and effective treatment.

If we imagine a technology that records that a person has recently been sleeping less, staying inside their home more, and has a faster-than-usual heart rate. These changes may be so subtle that the individual and their loved ones have not yet noticed them. Machine-learning algorithms may be able to make sense of these data, mapping them onto the individual’s past experiences and the experiences of other users. The technology may then be able to encourage the individual to engage in certain behaviors that have improved their well-being in the past or to reach out to their physician. 

If implemented incorrectly, it’s possible that this type of technology could have adverse effects. If an app alerts someone that they’re headed toward a deep depression, that could be discouraging information that leads to further negative emotions. 

Researchers say, “ what could be effective is a tool that could tell an individual ‘The reason you’re feeling down might be the data related to your sleep has changed, and the data relate to your social activity, and you haven’t had any time with your friends, your physical activity has been cut down. The recommendation is that you find a way to increase those things”. 

Artificial intelligence and machine-learning algorithms can make connections and identify patterns in large datasets that humans aren’t as good at noticing.