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I did a project like this early 2000s, and it's amazing how far you get by just combining frequency filtering and knn-clustering. Nothing fancy required. really.

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To your point: https://twitter.com/ShalitUri/status/1009534668880928769

The recent paper out from Google, "Scalable and accurate deep learning with electronic health records", has an notable result in the supplement: regularized logistic regression essentially performs just as well as Deep Nets

Another virtue of non-fancy methods is that they can provide a basic explanation of their reasoning, which is very important in the medical field. I think one of the main drawbacks of deep learning is its opacity.

You can do k-NN in the feature space of the network (activation of one of the last layers) to generate partial "explanations" of the neural network decisions.

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