Machine learning uses neuroimaging data to accurately diagnose depression

Written by Jonathan Wilkinson

Researchers from University of Texas at Austin (TX, USA) have used a supercomputer to train a machine-learning algorithm to identify signs of depression from neuroimaging data. The algorithm is able to recognize commonalities among hundreds of patients and classify them as being either healthy or showing features of mental illness. The findings were published in Psychiatry Research: Neuroimaging, and demonstrated that the machine-learning approach could classify individuals with major depressive disorder with roughly 75% accuracy. Neuroimaging data has long been used to study mental illness, particularly by looking at the relationship between brain function and structure. David Schnyer (University of...

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