AI detects signals for mental health assessment
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.”