The Intersection between Data Science and Mental Health

As data science rapidly becomes a key tool to improving efficiency in various fields, it’s important to cover its application in psychology. Mental health issues continue to affect millions everyday yet there are very limited ways to be able to foresee these problems in anothers. Unless individuals specifically reach out to others to express their problems, it’s very hard to understand what others are going through. Although data science isn’t as pervasive in mental health as it is in other industries like the automative, there are positive signs indicating that it soon can have an extremely positive impact. In todays blog, I will be discussing the exploration of how data science can, and already is, being used to help diagnose people who may be suffering from mental health issues.

According to an article from Fast Data Science, data scientists have already begun using machine learning models to analyze enormous sums of data sets related to mental health. By analyzing these chunks of data, their goal is to hopefully recognize a pattern that can point to the causes and syntoms of mental illness, allowing them to decide the best treatment since each illness is unique. Moreover, the article also cites examples of case studies done in the field. One case study I found interesting was one where researchers, using EHR and responses from a depression questionnaire, built an analytical model that could predict the risk of suicide within 90 days of visiting. This model allowed the researchers to predict which patients were at the highest risk based on past data like substance abuse history, suicide attempts, and questionnaire scores. I believe these types of models can be extremely benficial for society in the future and are the key to being able to intervene before it’s too late.

Looking toward the future, data scientists continue to make strides to improve machine learning models so they can be more efficient and widely used in assisting people who may be at risk of suicide. In addition to the algorithms used to predict people at risk, investments are being made for projects that can also analyze the genetic triggers related to mental illness. With tools like the algorithms and even ai – powered therapists, which has been proven to be more effective because patients feel more comfortable sharing personal information to a robot, the developoment of suicide risk machine learning looks very bright. Not only are these models going to save lives, they will also be very practical as they will be more accessible and they will be very cost-effective for health companies.

As data science rapidly becomes a key tool for improving efficiency across various fields, its application in psychology is particularly promising. Mental health issues continue to affect millions daily, yet our ability to foresee these problems in others remains limited. With these innovations slowly becoming more and more common, we are taking a huge step in suicide prevention. Ultimately, the most crucial aspect of these developments will be the data collection, as more data leads to greater accuracy and efficiency in these models but these will come as time passes. Nonetheless, the future of these projects look extremely optimistic and hopefully we will see more and more highly accurate models that are capable of preventing suicide before it even begins.