Assertion Detection in Multi-Label Clinical Text using Scope Localization
Table 2: Distribution of Assertion classes in the data.
['[BOLD] Class', '[BOLD] Model  [BOLD] Baseline', '[BOLD] Model  [BOLD] Baseline', '[BOLD] Model  [BOLD] Scope Localization model', '[BOLD] Model  [BOLD] Scope Localization model']
The annotations were done using BRAT tool Rules for annotation were generated after consulting with the Radiologist supervising the annotators. Other Radiologists were consulted to annotate any mentions that were previously unseen or ambiguous and also for the final review. For a fair comparison with the baseline, the box predictions from our model are converted to a sequence of labels per token. On first impressions, the performance seem to be affected by the quantity of data available for training with the best performance on present class and least performance on AWSE class. After further analysis, it appears that the scope lengths found in the training set is also a crucial factor. As shown, model performance for the present class declines with scope lengths 7, 10, and 20, which reflect sparsity of this class for these scopes in the training set. In contrast, the model performs well on the hypothetical class with scope length 7, reflective of the better distribution of this class for this scope relative to other scopes.