Thanks so much for your replies! I'll definitely check out some of those articles.
To provide a little background, I'm coming into this from the clinical risk prediction side of things, and have recently started to expand into using machine learning and deep learning approaches in EHR data. My prior work was mostly with cohort data using Cox models. We've been playing around with Keras run in Python in Jupyter Notebook on the Google Cloud. Thanks to efforts by @mgkahn an entire copy of our EHR is available in the Google Cloud, so we can run models basically in the same location as the data.
My sense is that much like image recognition, EHR data has an underlying structure reflecting the types of patients and visits that in theory could be uncovered using deep learning methods. How much one EHR data might be similar or different from another (Korea, etc.) is an interesting question that could reflect treatment approaches, although ultimately patients should have some underlying similarity and so I (perhaps naively) suspect that there should be a way to capture this structure using similar approaches (CNN?). Anyway, we're just getting started building our own models, but once we have something I'd definitely be open to sharing (as long as it's within the confines of de-identified data, etc.). We've done a run with 3 layer stacked autoencoder so far, but that wasn't in OMOP CDM.
Anyone else who might be interested, please feel free to post. Also any additional suggestions are more than welcome. I'm not sure whether the forum will be the long-term best way to move forward, but seems to be quite useful thus far. Thanks!