Real time data using sklearn
It is feasible to train the model from a static dataset and predict classifications for incoming data with the model. Retraining the model with each new set of patient data not so much. Also breaks the train/test mode of testing a ML model.
Trained models can be saved to file and imported in the code used for real time prediction. 
In python scikit learn, this is via the pickle package.
R programming saves to an rda object. saveRDS
yay... my first answering a ML question!
I have a real time data feed of health patient data that I connect to with python. I want to run some sklearn algorithms over this data feed so that I can predict in real time if someone is going to get sick. Is there a standard way in which one connects real time data to sklearn? I have traditionally had static datasets and never an incoming stream so this is quite new to me. If anyone has sort of some general rules/processes/tools used that would be great.