New machine learning technique can boost treatment for arthritis in kids
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Researchers have developed a novel machine learning (ML) technique that could predict the severity of arthritis in children, eliminating unnecessary treatments and potential side effects, and paving the way for customised care.
Arthritis, which can affect children besides the elderly, occurs when the immune system mistakes the body's own cells for foreign invaders, attacking the lining of the joints to cause swelling, pain and possibly long-lasting damage.
The findings, published in the journal PLOS Medicine, showed that the algorithm was able to classify patients into seven distinct groups according to the patterns of swollen or painful joints in the body.
It also accurately predicted which children will go into remission faster and which ones will develop a more severe form of the disease.
"Knowing which children will benefit from which treatment at which time is really the cornerstone of personalised medicine and the question doctors and families want answered when children are first diagnosed," said Rae Yeung, Professor at the University of Toronto in Canada.
"Identifying this group of children early will help us target the right treatments and prevent unnecessary pain and disability from ongoing active disease," said Yeung.
For the study, the team analysed the clinical data of 640 children.
Currently, there is no cure and the treatment consists of progressively more aggressive and costly medications.
Although the treatment is very effective in some children, it is also very expensive. Also, it is not clear what the long-term effects are.