A given patient may have multiple diagnoses, and thus multi-label learning is required.
we consider an open-access dataset, MIMIC-III (Johnson et al., 2016), which AUC F1 Model Macro Micro Macro Micro P@5 Logistic Regression 0.829 0.864 0.477 0.533 0.546 Bi-GRU 0.828 0.868 0.484 0.549 0.591 CNN (Kim, 2014) 0.876 0.907 0.576 0.625 0.620 C-MemNN (Prakash et al., 2017) 0.833 ---0.42 Attentive LSTM (Shi et al., 2017) -0.900 -0.532 -CAML (Mullenbach et al., 2018) 0.875 0.909 0.532 0.614 0.609 LEAM 0.881 0.912 0.540 0.619 0.612 Table 5: Quantitative results for doctor-notes multi-label classification task.