Medical Extractive Question-Answering Based on Fusion of Hierarchical Features.

With the combination of natural language processing and artificial intelligence techniques, medical extractive question-answering (Q&A) provides valuable insights and assists medical professionals in daily work and scientific research, answering medical questions rapidly and accurately, thus holding significant practical significance. Therefore, research on medical extractive Q&A holds significant practical significance. However, the current state of medical extractive question-answering lacks attention to the interaction and prediction layers in the model structure. To address these issues, this paper proposes the Integrating pre-trained multi-layer structural feature information based Bio-BERT (IPMF-Bio-BERT) approach. This method leverages the rich word vector representations generated by the pre-trained Bio-BERT model, incorporating semantic and syntactic structural information to obtain multi-dimensional and complementary interactive feature information. Additionally, we introduce a flexible guidance network based on interactive information, which combines iterative and pointer network techniques to enhance the predictive performance of the question-answering model. We evaluate our proposed model on the specialized biomedical extractive question-answering BioASQ corpus. Experimental results demonstrate that the IPMF-Bio-BERT training strategy enhances the recognition and predictive capabilities of medical extractive Q&A, we establish new state-of-the-art results by outperforming existing approaches.