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Data science “valuable” to mental health research

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A review involving experts from the University of Stirling has suggested new ways in which big data could enhance the diagnosis and treatment of mental health conditions.

In a new paper, published in Nature Human Behaviour, academics demonstrate how data science is already being widely applied in mental health research – and how it may be used in the future.

The commentary found that big data resources provide a “wealth of opportunity” to those tackling mental health and mental health research, such as: understanding the causes of mental illness and its prevention; improving detection, screening and diagnosis; developing new treatments and improving existing ones; supporting people in living well with a mental health condition; and driving improvements in health and social care.

Professor Margaret Maxwell, Director of the Nursing, Midwifery, and Allied Health Professions Research Unit at Stirling, is an author on the commentary paper by the MQ Data Science Group, which brings together experts from research and health in a bid to transform the approach to tackling mental health.

“This paper describes what data science can offer mental health in terms of opportunities for research,” explained Professor Maxwell. “The ideas in this paper will appeal to a broad range of audiences – from patients, clinicians, computer scientists, data scientists, social scientists, health researchers and industry.”

Benefits

Data science – which has been defined as the “fourth paradigm” of science, alongside empirical, theoretical and computational science – is already benefitting several areas of medicine, including heart disease prevention and cancer treatment, however, its use in mental health and neuroscience is still at an early stage.

The new paper uses the Health Research Classification System of the UK Clinical Research Collaboration, which divides health research under headings that underpin investigations into the cause, development, detection, treatment and management of diseases, conditions and ill health.

There is a growing interest in the physical environment and its relevance to mental health, for example, there are benefits to living in proximity to – and spending time in – green space. Among its findings, the paper suggests that longitudinal data on the environment could help in disease prevention and the promotion of wellbeing.

It says global positioning system technology could help monitor the routes people take through the environment and could encourage alterations – to encourage time spent in green spaces. Other technology could also provide vital data on latitude, sunlight exposure and ambient temperatures, which are linked to mental health.

The expert team said that information gathered from electronic health records and other digital sources – such as internet searches, social media and mobile phone data – provides “huge potential for monitoring of mental disorders and their treatment”.

“This could support the planning of services, implementation of interventions, evaluation of treatments, priority setting and the development of health policy and practice,” the paper added.

Screening

The analysis identifies the opportunity for an “automated screening process”, where data systems could analyse longitudinal clinical assessments and social context – alongside physiological, genetic and imaging data – to help diagnosis of conditions, such as depression.

It also suggests innovative disease management techniques, through wearable health devices and apps, highlighting technology that helps self-tracking of mood to facilitate treatment and supports patients, for example, in managing panic attacks.

“There is great potential to engage patients in their treatment and to move from sporadic patient contact towards continuous monitoring and guidance. In mental health, advances have been made in technology-assisted self-reporting and automated sensing,” it said.

The paper concludes: “Data science is a rapidly evolving field that offers many valuable applications to mental health research, examples of which we have outlined in this Perspective.

“Most importantly, it offers the possibility of making research incorporating real-world complexity tractable. We anticipate that the substantial advancements in mental health research we are beginning to see will bring tangible benefits to people with mental illness.”

The paper, How data science can advance mental health research, was led by Dr Tom Russ of the University of Edinburgh.

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