Temporal Self-Attention Network for Medical Concept Embedding
TABLE VIII: Mortality prediction performance on MIMIC III dataset.
['[BOLD] Ablation', '[BOLD] test loss', '[BOLD] test PR-AUC', '[BOLD] test ROC-AUC']
As can be seen from the table, the proposed model achieves the best performance compared to the ablated models on medical concept embedding. We observe the same trend as the ablated performance of unsupervised tasks, in which Multi_Sa+ outperforms Normal_Sa+. TeSAN outperforms the best ablated model by 2.75% on PR-AUC and 2.54% on ROC-AUC over MIMIC III.