Patient representation learning and interpretable evaluation using clinical notes
Table 1: Hyperparameters for stacked denoising autoencoder to learn dense patient representations, obtained after a randomized search. The default learning rate of 0.001 is used.
['Feature set', 'Number of layers', 'Hidden dimensions', 'Dropout proportion']
We use the sigmoid activation function for the encoding layers, and the linear activation function to decode real values. During the finetuning phase, we train the classifiers to minimize the categorical cross-entropy error using the same optimizer. We determine the number of layers, the dimensionality, and the dropout proportion also using a randomized hyperparameter search.