AI detects signals for mental health assessment

By Laura Berrill
AI can detect signals that are informative about mental health from questionnaires and brain scans

A study published today by an interdisciplinary collaboration, directed by Denis Engemann from Inria, has shown that machine learning from large population cohorts can yield “proxy measures” for brain-related health issues without the need for a specialist’s assessment. Researchers used the UK Biobank - one of the world’s largest and most comprehensive biomedical databases - that contains detailed and secure health-related data on the UK population. The work is published in the open access journal GigaScience.

Increase in global mental health conditions

Mental health issues have been increasing worldwide, with the WHO determining that there has been a 13% increase in mental health conditions and substance abuse disorders between 2007 and 2017. This impacts negatively nearly every area of life: school, work, family, friends, and community engagement. Among the many issues impeding the ability of society to address these disorders is that diagnoses of such health issues requires specialists and the availability of which ranges drastically across the globe. The development of machine learning methodology for the purposes of facilitating mental-health assessments could provide a much needed additional means to help detect, prevent and treat such health issues.

The results of the work demonstrate a future where psychologists and machine learning models could work hand-in-hand to produce increasingly fine-grained and personalised mental assessments. For example, in the future clients or patients may grant a machine learning model secured access to their social media accounts or their mobile phone data, to then return to measures that are useful to both the client and the mental health or education expert.

Human interaction still paramount

However, while AI can provide much needed assessment tools, human interaction will still be essential. Engemann stated: “What is not going to change is that mental health practitioners will need to carefully interpret and contextualise test results on a case-by-case basis and through social interaction, whether they are obtained using machine learning or classical testing.”

 

 

Share

Featured Articles

Are we ready to hand humanity’s future over to AI?

AI could contribute up to US$5.2tn to the global economy by the end of the decade, all in the name of sustainability

A watershed moment: feeding the world with AgriTech

Jordan MacPherson, the Director of Product Operations at Park Place Technologies, shares his insights into the growth of the global AgriTech market

5 minutes with: Vikram Saxena, CEO, BetterCommerce

We spoke to Vikram Saxena, the CEO of BetterCommerce, to learn his predictions for the future of AI, and his company’s role in this technology transition

AI helps handle post-Covid ecommerce explosion, says EY

AI Applications

How AI supported the fight against COVID-19

AI Applications

IBM, Canonical & Hitachi Vantara: IoT and the supply chain

Technology