Is there any suggested neural network for repeated-measures design?

Question

I have not found any resource on how to use a neural network to model measurements from a repeated-measures design. Let's say we have a small dataset (for example, 14 independent variables, 15-repetitions, and 50 subjects) of measured continuous variables as independent variables, plus some constant-valued independent variables such as age and sex. We also have one dependent variable measured at each repetition. I have tried to use a feedforward neural network with 10 neurons in one hidden layer to model this, and one-subject-out cross validation, but I think it can be improved maybe by following techniques: 1. Discretization of continuous input variables, since the dataset is small. 2. Using a feedback in training the network since a dependent variable (output) for each subject could be affected by the output from preceding outputs. 3. Remove redundant input variables using feature selection approaches.

The main problem is that I do not know how to input the constant-valued variable (age) to this model. And how to put this feedback into the model. Finally if I got the model accurate enough in one-subject-out validation, is it fine to average the weights and test this new model on the dataset and if it was good enough use the model for unseen data?

Answer 1

I've been struggling with the same problem and finally stumbled across an article entitled "Subject Specific Treatment to Neural Networks for Repeated Measures".

They recommend one-hot encoding the subject ID and using that as an additional input to the model (and they specifically suggest that it's better to provide the input to a deeper hidden layer, not the first). However, if you need to make predictions on new data, it's not exactly clear how best to do that. Their solution strikes me a pretty clever, and it is probably worth testing your architecture with and without the subject ID input.