With technology advancing, doctors are trying to find new ways to help those with mental health problems in a new and more effective way. Even by receiving constant trainings and gaining certifications and ACLS renewal, mental health is still a big concern for professionals in the medical field and many feel that it isn’t being addressed as it should. In response to this need, many healthcare personnel are turning towards big data to find the solution. Big data is being brought into the medical field through the use of various projects, social media, app development, and surveys and algorithms. Working to aid patients with mental health issues is just one more way big data is improving the healthcare industry.
Projects
Studies and projects involving big data have been created to help doctors and researchers better understand the issues that surround mental health illnesses. One specific example of a project is the World Well Being Project. This project is discovering new ways big data can be used to augment the population level of mental health care needed and provided. This project works by using studies, statuses, and other forms of big data. This project takes subjects and users and asks them to select a word or phrase that best expresses their personality. The study focuses on the differences between the users personality traits to compose a Big Five model. From this, the program incorporates its findings to better study psycholinguistic hypotheses. Researchers have found a high correlation between words characteristic of negative emotions and heart disease mortality figures. These results were more highly correlated than official socio-economic, demographic, and health statistics.
Social Media
It is no secret that social media has had a large affect on society, especially the younger generation. Knowing this, researchers and doctors have started using social media platforms to monitor the population’s health and to also help identify any risk factors for individuals with mental health issues. Social media platforms used within these studies including Facebook, Twitter, and Reddit. For population health domain, a large set of tweets with hints of depression are collected and then used to train a Machine Learning algorithm that aids in identifying depression-indicative tweets. The findings on depression data are then sent and analyzed by the Centers for Disease Control.
The data collected from Twitter and other social media platforms was used to investigate experiences of postpartum depression in new mothers to help identify risk factors for more specific individual domain. Researchers discovered that using Machine Learning techniques and big data to analyse behaviour patterns could predict emotional and behavioural changes with a 71% accuracy.
App Development
Not only is big data is being used to augment mental health care at a social level, but it is also being used on a more individual level. Researchers at several different university campuses are developing apps that are able to monitor sleep and activity patterns in order to combat depression among their college students. When an app identifies behaviors that match symptoms of depression, a counsellor on campus is alerted about the person and the possible symptoms them may have. Some of these symptoms include irregular movement and physical activity, disturbed and abnormal sleep patterns, social isolation, even a drop in regular class attendance. These apps are then able to give quick, real-time suggestions and activities similar to what a counsellor would advise. The data is also analyzed and then transmitted to counsellors on the campus so they can keep better track on the health of their students and extended help as needed.
Surveys and Algorithms
With social media platforms and app development, surveys and algorithms are involved in bringing big data into the mental health of others. In a world full of both individual and group therapy, big data project managers are determined to add to the information being discovered and stored. Treatment involving feedback are psychotherapy metrics drawing on historical data can help predict when the individual is at risk of their condition worsening. These metrics contain surveys that clients are asked to fill out as part of their therapy. These surveys detail the individual’s progression as they go through therapy. The algorithm's job is to predict which clients or individuals have a higher risk of relapsing in their condition or dropping out of therapy. Using big data, these surveys and algorithms provide feedback about the condition, the results of treatments and if they need to be adjusted.
One in four Americans are affected by mental health issues and illnesses. This especially applies to today’s youth as it is becoming harder and harder for the younger generation to focus on school, work, friends, and family. A study was done on the mental health of children 6 to 18 years of age between the years 2003 and 2013. Researchers found that there has been a rise of the use of antidepressants and depression symptoms have doubled in the younger generation. With these new discoveries, the study suggest that 70% of children do not receive the mental health care they need at such a young age.
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