Efficient Techniques for Processing Medical Texts in Legal Documents Using Transformer Architecture

The extensive utilization of medical texts in legal documents has led to the emergence of a significant research topic: the development of efficient methods for processing these texts in order to extract key medical information. This paper puts forth an enhanced model based on the Transformer architecture with the objective of optimising the processing efficiency of medical texts in legal documents. Based on the existing Transformer model, we have devised a multi-level attention mechanism that combines multi-task learning and domain adaptation technology. This mechanism enables the simultaneous capture of the association between medical terms and legal language in the text, thereby enhancing the model's ability to comprehend complex legal language. Furthermore, domain knowledge graph-assisted training is employed to enhance the precise matching of medical and legal terminology by the model. The experimental results demonstrate that the proposed model markedly enhances the precision and efficiency of information extraction in diverse medical text processing tasks, outperforming the traditional model.