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Table 5 The effect on prediction performance of different graph encoders

From: KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning

Model

Encoder in HG

Encoder in RG

Prediction Performance

GAT

GCN

GAT

GCN

Jaccard

F1

PR-AUC

\(\text {KAMPNet}_{AC}\)

✓

✗

✗

✓

0.4973

0.6454

0.7195

\(\text {KAMPNet}_{AA}\)

✓

✗

✓

✗

0.4878

0.6381

0.7160

\(\text {KAMPNet}_{CC}\)

✗

✓

✗

✓

0.4869

0.6373

0.7176

\(\text {KAMPNet}_{CA}\)

✗

✓

✓

✗

0.4853

0.6358

0.7122