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I am not sure this type of model is a good use case for the particular task. More specifically, citing Chollet from Deep Learning with Python book, Always remember that when it comes to markets, past performance is not a good predictor of future returns—looking in the rear-view mirror is a bad way to drive. Machine learning, on the other hand, is applicable ...


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Of course that all weights are the same, but the update applied to the weights has a contribution from each of the timesteps, and the contribution associated with the first timesteps is what is more affected by the vanishing gradient problem.


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you need to define how many time steps you want to have in each time series block. then for each unique patient, you need to create these blocks so the training set going to be a 3D matrix, and the dimensions are: number of blocks * number of time steps * number of features in addition to time series data, you can also add another head to feed NN with the ...


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50% is quite decent because you have five labels and random guessing model would have achieved only 20% accuracy. So you know your model is learning something. The other thing you want to check out is whether this is suited to be a regression problem more than classification. For e.g, misclassifying a 5 (ground truth) into a 4 is better than misclassifying ...


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