pyhealth.models.GAMENet#

The separate callable GAMENetLayer and the complete GAMENet model.

class pyhealth.models.GAMENetLayer(hidden_size, ehr_adj, ddi_adj, dropout=0.5)[source]#

Bases: Module

GAMENet layer.

Paper: Junyuan Shang et al. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination AAAI 2019.

This layer is used in the GAMENet model. But it can also be used as a standalone layer.

Parameters:

  • hidden_size (int) – hidden feature size.

  • ehr_adj (tensor) – an adjacency tensor of shape [num_drugs, num_drugs].

  • ddi_adj (tensor) – an adjacency tensor of shape [num_drugs, num_drugs].

  • dropout (float) – the dropout rate. Default is 0.5.

Examples

>>> from pyhealth.models import GAMENetLayer
>>> queries = torch.randn(3, 5, 32) # [patient, visit, hidden_size]
>>> prev_drugs = torch.randint(0, 2, (3, 4, 50)).float()
>>> curr_drugs = torch.randint(0, 2, (3, 50)).float()
>>> ehr_adj = torch.randint(0, 2, (50, 50)).float()
>>> ddi_adj = torch.randint(0, 2, (50, 50)).float()
>>> layer = GAMENetLayer(32, ehr_adj, ddi_adj)
>>> loss, y_prob = layer(queries, prev_drugs, curr_drugs)
>>> loss.shape
torch.Size([])
>>> y_prob.shape
torch.Size([3, 50])
forward(queries, prev_drugs, curr_drugs, mask=None)[source]#

Forward propagation.

Parameters:

  • queries (tensor) – query tensor of shape [patient, visit, hidden_size].

  • prev_drugs (tensor) – multihot tensor indicating drug usage in all previous visits of shape [patient, visit - 1, num_drugs].

  • curr_drugs (tensor) – multihot tensor indicating drug usage in the current visit of shape [patient, num_drugs].

  • mask (Optional[tensor]) – an optional mask tensor of shape [patient, visit] where 1 indicates valid visits and 0 indicates invalid visits.

Returns:

a scalar tensor representing the loss. y_prob: a tensor of shape [patient, num_labels] representing

the probability of each drug.

Return type:

loss

training: bool#
class pyhealth.models.GAMENet(dataset, embedding_dim=128, hidden_dim=128, num_layers=1, dropout=0.5, **kwargs)[source]#

Bases: BaseModel

GAMENet model.

Paper: Junyuan Shang et al. GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination AAAI 2019.

Note

This model is only for medication prediction which takes conditions and procedures as feature_keys, and drugs as label_key. It only operates on the visit level. Thus, we have disable the feature_keys, label_key, and mode arguments.

Note

This model only accepts ATC level 3 as medication codes.

Parameters:

  • dataset (SampleEHRDataset) – the dataset to train the model. It is used to query certain information such as the set of all tokens.

  • embedding_dim (int) – the embedding dimension. Default is 128.

  • hidden_dim (int) – the hidden dimension. Default is 128.

  • num_layers (int) – the number of layers used in RNN. Default is 1.

  • dropout (float) – the dropout rate. Default is 0.5.

  • **kwargs – other parameters for the GAMENet layer.

generate_ehr_adj()[source]#

Generates the EHR graph adjacency matrix.

Return type:

tensor

generate_ddi_adj()[source]#

Generates the DDI graph adjacency matrix.

Return type:

tensor

forward(conditions, procedures, drugs_hist, drugs, **kwargs)[source]#

Forward propagation.

Parameters:

  • conditions (List[List[List[str]]]) – a nested list in three levels [patient, visit, condition].

  • procedures (List[List[List[str]]]) – a nested list in three levels [patient, visit, procedure].

  • drugs_hist (List[List[List[str]]]) – a nested list in three levels [patient, visit, drug], up to visit (N-1)

  • drugs (List[List[str]]) – a nested list in two levels [patient, drug], at visit N

Returns:

loss: a scalar tensor representing the loss. y_prob: a tensor of shape [patient, visit, num_labels] representing

the probability of each drug.

y_true: a tensor of shape [patient, visit, num_labels] representing

the ground truth of each drug.

Return type:

A dictionary with the following keys

training: bool#