It’s a sad fact that children can develop arthritis, and while some end up going into remission, the disease becomes much worse in others. A new machine-learning technique is reportedly able to predict which kids will fall into which category, allowing for their treatment to be tailored accordingly.
What it discovered was that most of the children could be sorted into one of several groups, depending on the area of their body in which the joint pain was present – those areas included the pelvic region, fingers, wrists, toes, knees and ankles. Some of the patients, however, didn’t fit neatly into one group, as their joint pain wasn’t localized. It was these children who took longer to go into remission, ultimately faring worse than the others.
It is now hoped that as soon as a patient is identified as belonging to that last non-group, doctors can set about administering fairly potent medications, perhaps improving the outcome. On the other hand, if it’s determined that a child is likely to soon enter remission anyway, the use of medication can be minimized – this will both reduce costs, and spare the patient from unnecessarily enduring side effects.
A paper on the research, which also involved Prof. Quaid Morris and recently-graduated student Simon Eng, was published this week in the journal PLOS Medicine.
Machine learning used to improve outcome for arthritic kids [New Atlas]