Thanks for sharing Patrick. I am very interested in the algorithmic pipeline they use for this including the deep learning part. As you know we have started with increasing our deep learning experience at EMC with a new postdoc and collaboration with NYU.
Regarding storing other type of data in the CDM, I think there is indeed an interesting area to work on. I know that at EMC a lot of work is done on predictive image analysis and combining this with other clinical information is been explored intensively in some ongoing initiatives (see http://www.bigr.nl/website/). There have been some exploration on Transmart etc, but there is no reason not to look at OMOP CDM (actually there are very good reasons to do look at it )
Regarding the ECG analysis work we are doing there might indeed also be some opportunities. We are doing predictive analytics using ECG parameters in combination with clinical predictors. These ECGs are recorded periodically from all 15,000 persons in a large cohort that is being followed in Rotterdam (started in 1991, see http://www.epib.nl/research/ergo.htm). Currently we use the automatically extracted ECG parameters (using a in house build computer program) for prediction. ERGO is not in the OMOP CDM but if it would be and we would have a way to store the derived variables or the ECG signals themselves this would open some big opportunities. Think for example about running a predictive study that incorporates ECG derived measurement together with others that have ECG data would be an unique opportunity. This would be awesome!
I also think that @rwpark and his group would love to have a way to add the ECG information for there predictive work. I know that they are storing ECG signals in a continuous manner from the ICU and also have paper scans from the Hospital. You could store the images (less preferred I think in the ECG case), the signals (would allow pattern recognition tools to run on top of OMOP CDM), or only te derived data.