I need to make a model that predicts certain medical outcomes based on the answer to health-related questionnaires. Providers have patients fill out these questionnaires more than once, at irregular time intervals - for example, a patient may fill out one questionnaire at the beginning of their care episode, another 34 days later, another 12 days after that, and so on.
I have a pretty good idea of how to handle the outcomes I'm predicting; I can either use survival modeling techniques or treat it as a binary classification problem within a certain time window. However, I'm at a bit of a loss when it comes to figuring out how to incorporate the irregularly repeated input features:
Traditional time series techniques assume observations at regular intervals.
Recurrent neural networks usually make similar assumptions.
By-patient deduplication or aggregation is the easiest solution, but I would rather than throw out most of my data right up front.
Does anyone know of models or strategies that are useful for this kind of problem?