Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
Table 2: Models performance (%) on the test set.
['Model name', 'F1macro', 'F1weighted', 'MRR', 'Hit@1', 'Hit@3', 'Hit@5', 'Hit@10']
The significant increase in F1\textsubscriptmacro for the proposed models (4%) combined with increase in F1\textsubscriptweighted (1%) compared to baseline RuBERT model show that our proposed models performance gains are due to better classification of less frequent disease classes. Note that, although the number of classes increased almost by the factor of four for the RuPool-BERT 1k model, the performance measured by the Hit@3 metric degrades significantly slower (42.97 vs. 70.14).