Input with variable length Classification problem

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?

• I have had similar problems to solve, where it's a product category in place of number of visit. I have used one-hot-encoding to address varying counts of category.value_counts. – bkrishna2006 Jun 11 '20 at 13:22
• Can you elaborate more on how you converted the variable length input to a fixed length? – Born2Code Jun 11 '20 at 15:06

For visit t for patient i I would predict $$y_{it} = f(x_{it-1})$$. So I would only look back to the previous visit of the patient.