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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 data set 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?

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  • $\begingroup$ Hello! Can you please put your main question in the title? Btw, if you haven't read it yet, I suggest you read ai.stackexchange.com/help/on-topic, so that you familiar with our scope. $\endgroup$ – nbro Jun 11 at 12:19

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