A team of scientists is looking to harness the power of “big data” to develop a new methodology and visualization tools to cluster patients with mood disorders. Their aim is to group patients based on comprehensive data profiles, which include genetic, environmental, demographic and clinical data, rather than DSM criteria, which could improve treatment decisions.
Led by Rachael Hageman Blair, PhD, of the University of Buffalo School of Public Health and Health Professions, in New York, the research team includes biostatisticians, information scientists, mathematicians and psychiatrists. They note that studies from the National Institute for Mental Health indicate that treatments for mood disorders are less than 25% effective.
“Our aim is to ignore the DSM label and regroup patients based on comprehensive data profiles, which include genetic, environmental, demographic and clinical data, among others,” Blair said in a statement. “Some groups of individuals may be more responsive to treatment, which is important for precision medicine.”
The next step for the team is to develop the methodology.
Mood disorders like depression are common among U.S. adults. Still, such disorders remain challenging for clinicians to diagnose and treat effectively.
A public health researcher at the University at Buffalo is part of a team of scientists that received a National Science Foundation (NSF) grant to use big data to develop a new approach they say will improve the classification of mood disorders, leading to more effective outcomes for psychiatric patients.
Their aim is to use big data to develop a novel methodology and visualization tools to cluster patients with mood disorders.
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