Known periods of missing data for deep learning applications
Depends on your application.
One way to deal with it is to remove any samples with missing features from the train set.
Of course, at test-time, the predictions will not be reliable, as it's an unseen situation. So it's your task to detect those conditions at test-time and tell the user the predictions are not reliable.
Another option:
If the features that might be missing are categorical, an "unknown" category might be just fine to train on. For numeric features that doesn't work.
I am using time series data from surgery to label a patient (binary) as having a certain medical condition or not as the patient is discharged from surgery. I am hoping to use LSTM or RNN. However, there are certain features that are missing in the sequence when the patient goes on bypass (for example, there is no pulse because the heart is stopped). Thus, all my patients have 'missing data' at some point. Should I still use imputation or is there a way that I can still use these features with their known gaps?