Social media, machine learning and mental health - Activate World

Social media, machine learning and mental health

Storyline:

Telehealth is the use of digital technologies to deliver medical care, health education and public health services by connecting multiple users in separate locations. Telehealth encompasses a broad definition of technology-enabled health care services.

Telemental health—or mental health services provided from a distance—is one of the fastest growing sectors within the telehealth space. The National Business Group on Health reports that 56% of employers plan to offer telemental health services to their employees in 2018, which is double the number in 2017.

An important development in this area is the combination of machine learning algorithms that diagnose mental illness with user-data gathered from social media websites. For years, psychologists used pattern recognition models with little success to determine whether a patient is at risk of developing a mental illness or committing suicide.

Machine learning algorithms are effective because they can process large amounts of data and distill that data down into usable formulas to meet the desired purpose. Machine learning algorithms can extract insight, discover anomalies, recognize patterns and make predictions. Among these uses, pattern recognition and predictions are important in diagnosing mental illness or predicting suicide risk. The inherent advantage of machine learning algorithms is the use of artificial intelligence to automatically learn and improve based on experience.

A recent study which used machine learning algorithms to predict the risk of patient suicide found that the algorithm’s prediction accuracy was sixty to eighty percent and could predict potential suicide attempts as far as 720 days out.

Noteworthy:

  • Mental health is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be $2.5 trillion in 2010 and expected to double by 2030.
  • Accurate suicide prediction requires analysis of hundreds of factors, including race, gender, age, socio-economic status, physical and mental medical history, and other relevant information.
  • The use of social media and big data for health applications is a rapidly growing area of research variously referred to asinfoveillance, digital epidemiology, and digital disease detection.

Quests and Actions (Q&A):

  • With 800,000 suicide deaths worldwide every year – isn’t this a public health issue that cannot be ignored?
  • There is always a possibility that patients could be misdiagnosed by a phone app or social media study and experience harm. Who is responsible in that situation?
  • There are major privacy issues for social media users who are being monitored without their knowledge. How can privacy and mental health concerns both be addressed in these research efforts?
Sources: ForbesGeorgetown Law Technology Review, Healthcare IT News, QuartzWired
Photo by NordWood Themes on Unsplash