# Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning
###### Abstract  
With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users' intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts. multi-task learning; natural language processing; BERT; BiLSTM; BiGRU; DPCNN +
Footnote †: journal: Applied sciences  
methods include logical regression, support vector machine (SVM), and naive Bayesian (naive Bayes), as well as long-term and short-term memory network (LSTM), gated loop unit (GRU), and convolution neural network (CNN) based on deep learning. Although these models have achieved some success in classification, emotion analysis, and other tasks, they do not perform well in dealing with medical texts with complex grammatical structures and semantic dependencies, and it is difficult to deal with named entity recognition (NER), intention recognition, and other tasks that require context understanding. With the development of deep learning technology, large language models (LLMs) such as the GPT series, BERT, T5, and PaLM have become a new solution [1]. They show powerful language understanding and generation ability through large-scale pre-training and a deep self-attention mechanism, and are widely used in named entity recognition, intention recognition, and other tasks. [PERSON] et al. [2] proposed a medical question answering system based on LLMs, which achieved high accuracy and reliability on multiple medical question answering datasets through prompt strategies and integration methods; however, there were still problems of performance degradation and insufficient interpretation ability in specific situations. Although mainstream large language models such as GPT and LLMaA perform well in a variety of NLP tasks, their training and fine-tuning processes are highly dependent on computational sources, especially for high-complexity tasks such as Chinese medical text processing, so large models require massive quantities of training data and powerful computational resources. It consumes a lot of manpower, material, and financial resources, which are difficult for ordinary research personnel to bear. In addition, in the Chinese medical field, the sensitivity of patient data requires us to pay extra attention to data privacy and security issues, and LLMs often need to upload data to the cloud for training, which may pose a potential privacy breach risk. Considering the lack of transparency in the process of large model reasoning, its application in medical decision-making is also limited due to the lack of explanation. Based on these considerations, in order to improve the performance of the system with limited training data and computing resources, this paper proposes a medical text parsing method based on a multi-task learning framework.