Models# We implement the following models for supporting multiple healthcare predictive tasks. pyhealth.models.MLP MLP MLP.mean_pooling() MLP.sum_pooling() MLP.forward() MLP.training pyhealth.models.CNN CNNLayer CNNLayer.forward() CNNLayer.training CNN CNN.forward() CNN.training pyhealth.models.RNN RNNLayer RNNLayer.forward() RNNLayer.training RNN RNN.forward() RNN.training pyhealth.models.Transformer TransformerLayer TransformerLayer.forward() TransformerLayer.training Transformer Transformer.forward() Transformer.training pyhealth.models.RETAIN RETAINLayer RETAINLayer.reverse_x() RETAINLayer.compute_alpha() RETAINLayer.compute_beta() RETAINLayer.forward() RETAINLayer.training RETAIN RETAIN.forward() RETAIN.training pyhealth.models.GAMENet GAMENetLayer GAMENetLayer.forward() GAMENetLayer.training GAMENet GAMENet.generate_ehr_adj() GAMENet.generate_ddi_adj() GAMENet.forward() GAMENet.training pyhealth.models.MICRON MICRONLayer MICRONLayer.compute_reconstruction_loss() MICRONLayer.forward() MICRONLayer.training MICRON MICRON.forward() MICRON.training pyhealth.models.SafeDrug SafeDrugLayer SafeDrugLayer.pad() SafeDrugLayer.calculate_loss() SafeDrugLayer.forward() SafeDrugLayer.training SafeDrug SafeDrug.generate_ddi_adj() SafeDrug.generate_smiles_list() SafeDrug.generate_mask_H() SafeDrug.generate_molecule_info() SafeDrug.forward() SafeDrug.training pyhealth.models.Deepr DeeprLayer DeeprLayer.forward() DeeprLayer.training Deepr Deepr.forward() Deepr.training