Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings
Table 1: Accuracy of existing methods and our proposed model across CADEC, PsyTAR and SMM4H2017 datasets. ⊥ - concept embeddings are randomly initialized and then updated during training.
['Method', 'CADEC', 'PsyTAR', 'SMM4H17']
The first three rows represent existing systems and the next two rows represent our approach. Our model achieves the new state-of-the-art accuracy of 85.49%, 83.68% and 90.84% across the three datasets. Our model outperforms existing methods with accuracy improvement of 2.31%, 1.26% and 1.2% respectively. State-of-the-art results achieved by our model across three standard datasets illustrate that learning target concept representations along with input mention representations is simple and much effective compared to separately generating target concept representations using text or graph embedding methods and then using them.