Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network
Table 8: Performance comparison between the proposed methods and top-ranking approaches on the test data for DDI classification. Performance are measured through F1-Score for each class and Macro Average (MAVG). The highest scores are highlighted in bold.
['[ITALIC]  [BOLD] Models', '[ITALIC]  [BOLD] Advice', '[ITALIC]  [BOLD] Mechanism', '[ITALIC]  [BOLD] Effect', '[ITALIC]  [BOLD] Int', '[ITALIC]  [BOLD] MAVG']
Class wise Performance Analysis We observe that no single model outperforms others for all classes. FBK irst obtained the best performance for Int class. B-LSTM, AB-LSTM and Joint AB-LSTM obtained the best performance for Mechanism, Effect and Advice classes respectively. However, Joint AB-LSTM outperformed all other models on aggregate performance measure using macro-average F1 score. All models finds it easier to detect Advice interaction types compared to the instances of the other three interaction types. Similarly all models find it most difficult to detect Int interaction types. The worse performance on the Int class can be attributed to insufficient training data. Effect interaction class was found to be the second most difficult class to detect by most of the models compared in this analysis.