Hi @mgkahn and all,
I don't believe the Deep Patient approach handles temporal conditions. The focus is on representation learning from all data available for a given patient record in order to predict codes from a vocabulary of common diagnosis codes.
It would be really nice to have an approach that tackles the temporality on the representation side. We had done some work on incorporating longitudinal data looking a prediction of CKD using Kalman filters and topic models for notes and we (@aperotte, Rajesh Ranganath, Dave Blei, and I) found that (no surprise!) it helps to incorporate longitudinal data in a temporally aware fashion (more at https://academic.oup.com/jamia/article/22/4/872/1746401). Last year, we (same team) have looked into incorporating time, but on the side of predictions: In our Deep Survival Analysis paper (
http://proceedings.mlr.press/v56/Ranganath16.pdf) we compute a survival curve through time for risk of a specific code (CHD onset), given a particular snapshop of a patient's data in time (a month). Not exactly what you are looking for, I assume, but I think including temporality is a particularly exciting area of work, and deep learning approaches can help.