Machine learning could eliminate unnecessary treatments for children with arthritis

Arthritis is not just an ailment of old age — it can affect children too, causing lifelong pain and disability in its most severe forms. Fortunately, some kids grow out of it. Knowing which patients will develop milder forms of disease could spare them unnecessary treatment and potential medication side effects but currently doctors have no way of predicting disease course or severity.

That could now change thanks to a machine learning tool developed by Quaid Morris, a professor of computer science at the Donnelly Centre for Cellular and Biomolecular Research at the University of Toronto, Dr. Rae Yeung, Professor of Paediatrics, Immunology and Medical Science at the University of Toronto, and their recently-graduated, co-supervised student Simon Eng.

Morris is also faculty in the Vector Institute for Artificial Intelligence and is an inaugural AI Chair by the Canadian Institute for Advancement of Research. Yeung is also the inaugural Hak-Ming and Deborah Chiu Chair in Paediatric Translational Research at the Hospital for Sick Children (SickKids).

Writing in the journal PLOS Medicine, the researchers describe a computational approach based on machine learning, a form of artificial intelligence in which the computer learns to recognize recurrent patterns from a sea of data. The algorithm was able to classify patients into seven distinct groups according to the patterns of swollen or painful joints in the body. Moreover, it also accurately predicted which children will go into remission faster and which ones will develop a more severe form of disease. ..Read More..

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