7:08 AM
@Sam: For a RNN model, it is less about the number of samples, and more about repetition (same feature repeated at each time step means same thing) or having a changeable sequence length.
2 hours later…
8:42 AM
@NeilSlater; Thanks for the response. Personally I'd be tempted to build a simpler model for this problem but my employer wants us to attempt the RNN approach. I guess it makes us sound better. I'm a little confused with your following sentence:
*If your clinical measurements were different types at each time step, and the sequence was always same length, then probably no need for RNN.*
*If your clinical measurements were different types at each time step, and the sequence was always same length, then probably no need for RNN.*
For clarity, my clinical measurements will be of the same type in that they are numeric (not sure if this is what you mean). In terms of the sequence being the same length, are you referring to the number of time points for a given data point? i.e. 5 time-varying readings for each patient. I was under the assumption keeping the number of time points the same was a prereq for these type of models.
11 hours later…
7:33 PM
@Sam: I didn't mean numeric vs non-numeric, but for instance, are the same measurements taken repeatedly (e.g. blood pressure, heart rate etc), or different clinical measures taken each time. If they are the same (or mostly the same) then this is more sequence-like, so a better fit for RNN or time-distributed.
8:32 PM
@NeilSlater: The data is structured for measurements to be taken for each covariate at fixed time points. So yes, it is the same measurements being taken at different points. When you say flat input, are you referring to transforming subsequent readings as a separate covariate? I.e. bmi_t; bmi_t+1; bmi_t+2 for instance? Up until now, this is how I've been treating time-varying covariates. Thanks again.
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