ParTRE: A relational triple extraction model of complicated entities and imbalanced relations in Parkinson's disease

The relational triple extraction of unstructured medical texts about Parkinson's disease is critical for the construction of a medical knowledge graph. However, the triple entities in Parkinson's disease are usually complicated and overlapped, which impedes the accuracy of triple extraction, especially in the case of rarely available corpus. Therefore, this study first builds a corpus about Parkinson's disease. Then, a tagging-based three-stage relational triple extraction model is proposed, named ParTRE. To enhance the contextual representation of sentences, the proposed model employs BiLSTM modules to capture fine-grained semantic information. Additionally, a conditional normalization layer is used so that entity pairs can be extracted accurately from two complementary directions. As for the imbalanced relationship categories, an adaptive loss function strategy based on focal loss is derived by assigning different weights to relationship categories and reducing the loss of easy-to-classify samples. The model performance is evaluated on the Parkinson's corpus and public datasets. The results indicate that the proposed model achieves an overall F1-score of 93.3 % on the Parkinson's corpus and comparable performance on public datasets compared with the state-of-the-art methods. Moreover, a satisfactory result is achieved by the proposed model on conquering the overlapped entities and imbalanced relationship categories. Owing to demonstrated availability and validity, the proposed method can be integrated with medical knowledge graphs and therefore benefits medical intelligence.