Skip to main content

Table 2 Performance comparison on medication prediction task

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

Methods

Jaccard

F1

PR-AUC

\(\text {LR}\)

0.4075

0.5658

0.6716

\(\text {LEAP}\)

0.3921

0.5508

0.5855

\(\text {Retain}\)

0.4456

0.6064

0.6838

\(\text {GRAM}\)

0.4176

0.5788

0.6638

\(\text {GAMENet}^{-}\)

0.4401

0.5996

0.6672

\(\text {GAMENet}\)

0.4555

0.6126

0.6854

\(\text {G-BIRT}\)

0.4565

0.6152

0.6960

\(\text {GATE}\)

0.4742

0.6315

0.7087

\(\text {KAMPNet}\)

0.4973

0.6454

0.7195