Table 4.
From iDASH to MGH | From MGH to iDASH | ||||||||
---|---|---|---|---|---|---|---|---|---|
Subdomain | AUC | Precision | Recall | F1 | Subdomain | AUC | Precision | Recall | F1 |
Cardiology | 0.828 | 0.923 | 0.715 | 0.806 | Cardiology | 0.731 | 0.829 | 0.500 | 0.624 |
Gastroenterology | 0.802 | 0.396 | 0.691 | 0.503 | Gastroenterology | 0.832 | 1.000 | 0.664 | 0.798 |
Neurology | 0.877 | 0.745 | 0.859 | 0.798 | Neurology | 0.775 | 0.902 | 0.567 | 0.696 |
Psychiatry | 0.803 | 0.907 | 0.613 | 0.732 | Psychiatry | 0.941 | 0.794 | 0.900 | 0.844 |
Pulmonary | 0.820 | 0.197 | 0.692 | 0.307 | Pulmonary | 0.545 | 1.000 | 0.089 | 0.164 |
Nephrology | 0.770 | 0.573 | 0.561 | 0.567 | Nephrology | 0.634 | 0.750 | 0.273 | 0.400 |
The performance of using the best interpretable iDASH classifier to classify the medical subdomain of MGH clinical notes, and using the best interpretable MGH model to classify the medical subdomain of iDASH documents