Using Machine Learning to Diagnose Depression - DZone Big Data

Machine learning has been increasingly capable of accurately diagnosing a range of physical and mental health conditions in recent years. I've written previously about algorithms that monitor things like speech to detect the onset of conditions such as Alzheimer's, whilst applications have also used mobile phone data to detect changes in lifestyle and possible depression in individuals. A recent study from the University of Texas at Austin takes a slightly different tact by using AI to spot vulnerability to depression from brain imaging. The researchers worked with a supercomputer to train the algorithm to detect commonalities in MRI scans, genomic data and various other datasets relevant to depression and anxiety. It aims to improve upon previous work by researchers who have studied mental disorders via the relationship between brain function and structure in neuroimaging data.