Patient representation learning and interpretable evaluation using clinical notes
Table 2: Hyperparameters for feedforward neural network classifiers for different tasks and feature sets, obtained after a randomized search. The default learning rate of 0.001 is used.
['Task', 'Feature set', 'Number of layers', 'Hidden dimensions', 'Activation function']
We use the dense patient representations as input features to train feedforward neural network classifiers on multiple independent tasks. We evaluate the performance on a range of tasks to gain insight into the task independent nature of the representations, and the information encoded within the vectors. We disregard the instances that do not have a task label.