|Image: Welcome Images|
Friday, February 17, 2012
Computers to identify the at risk?
The other day I posted an article about the possibility that a blood test could help identify those with depression, a great step forward. Now it looks like the researchers are getting computers involved in identifying those at risk of mental illness.
A press release by the Welcome Trust suggests that a computer programme may be able to identify those at risk of mood disorders & anxiety. According to research published in PLoS ONE, an open access journal, computers can be 'taught' to differentiate between brain scans of adolescents to identify those most at risk of psychiatric disorders like depression and anxiety.
With most mental illnesses typically manifesting themselves during the adolescent years & early adulthood, the earlier that those at risk can be identified the better. Early intervention could result in a delay or even prevent the illness appearing in those at risk.
Hopefully these kinds of breakthroughs continue & result in efficient methods of identifying those at risk of mental illness. Early intervention can only be a good thing for those at risk. I know I wish I had known what was going on 30 years ago.
Read the full Welcome Trust press release.
Computer programs can be taught to differentiate between the brain scans of healthy adolescents and those most at risk of developing psychiatric disorders, such as anxiety and depression, according to research published yesterday in the open access journal ‘PLoS ONE’. The research suggests that it may be possible to design programs that can accurately predict which at-risk adolescents will subsequently develop these disorders.
Read the original report in PLoS ONE.
There are no known biological measures that accurately predict future development of psychiatric disorders in individual at-risk adolescents. We investigated whether machine learning and fMRI could help to: 1. differentiate healthy adolescents genetically at-risk for bipolar disorder and other Axis I psychiatric disorders from healthy adolescents at low risk of developing these disorders; 2. identify those healthy genetically at-risk adolescents who were most likely to develop future Axis I disorders.