Distilled Wasserstein Learning forWord Embedding and Topic Modeling
Table 2: Top-N procedure recommendation results for various methods.
['Method', 'Top-1 (%) P', 'Top-1 (%) R', 'Top-1 (%) F1', 'Top-3 (%) P', 'Top-3 (%) R', 'Top-3 (%) F1', 'Top-5 (%) P', 'Top-5 (%) R', 'Top-5 (%) F1']
The third task is recommending procedures according to the diseases in the admissions. In our framework, this task can be solved by establishing a bipartite graph between diseases and procedures based on the Euclidean distance between their embeddings. The proposed embeddings should reflect the clinical relationships between procedures and diseases, such that the procedures are assigned to the diseases with short distance. For the m-th admission, we may recommend a list of procedures with length L, denoted as Em, based on its diseases and evaluate recommendation results based on the ground truth list of procedures, denoted as Tm. In particular, given {Em,Tm}, we calculate the top-L precision, recall and F1-score as follows: P=∑Mm=1Pm=∑Mm=1|Em∩Tm||Em|, R=∑Mm=1Rm=∑Mm=1|Em∩Tm||Tm|, F1=∑Mm=12PmRmPm+Rm. We find that although our DWL method is not as good as the Word2Vec when the model is trained from scratch, which may be caused by the much fewer epochs we executed, it indeed outperforms other methods when the model is fine-tuned from Word2Vec.