Does it make any sense to apply convolution to inputs which have no order/distance between them? • /r/MachineLearning

I know that CNN's are used a lot for computer vision where we want to deal with local features of the image. This makes sense because one pixel influences how we interpret its neighbours. If we had data for, say, medical decisions and we recorded many variables like age, weight, and existing medical conditions, these inputs have no distance between them and no sense of order which we could use to identify nearest neighbours. Having said that I could imagine that it might be useful to use a CNN for the inputs because it groups together inputs in ways which are unlikely to occur by chance if we just trained a NN by stochastic gradient descent.