I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem).
The dataset looks something like below:
Patient#|Visit#|Other medical features related to patient and visit|label (disease stage)
So, I am interested in using patient's past visit data inorder to predict the current disease stage. But, the problem is that all the patients don't have equal number of visits. So, I can't just append the past visit information to predict the future visit label like below:
concat(Patient #n 1st visit (X = all input features)|label of this visit| Patient #n 2nd visit (X = all input features)) and then try to predict the label for 2nd visit using previous visit information.
In the above problem, the number of visit =1, but I have a varying number of visit for each patient. How can I tackle this problem?